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21.
Wang, Ning; Jajodia, Aditya; Karpurapu, Abhilash; Merchant, Chirag
Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors Proceedings Article
In: Roll, Ido; McNamara, Danielle; Sosnovsky, Sergey; Luckin, Rose; Dimitrova, Vania (Ed.): Artificial Intelligence in Education, pp. 372–377, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-78270-2.
@inproceedings{wang_charisma_2021,
title = {Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors},
author = {Ning Wang and Aditya Jajodia and Abhilash Karpurapu and Chirag Merchant},
editor = {Ido Roll and Danielle McNamara and Sergey Sosnovsky and Rose Luckin and Vania Dimitrova},
url = {https://link.springer.com/chapter/10.1007/978-3-030-78270-2_66},
doi = {10.1007/978-3-030-78270-2_66},
isbn = {978-3-030-78270-2},
year = {2021},
date = {2021-01-01},
booktitle = {Artificial Intelligence in Education},
pages = {372–377},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.
22.
Pynadath, David V.; Wang, Ning; Kamireddy, Sreekar
A Markovian Method for Predicting Trust Behavior in Human-Agent Interaction Proceedings Article
In: Proceedings of the 7th International Conference on Human-Agent Interaction - HAI '19, pp. 171–178, ACM Press, Kyoto, Japan, 2019, ISBN: 978-1-4503-6922-0.
@inproceedings{pynadath_markovian_2019,
title = {A Markovian Method for Predicting Trust Behavior in Human-Agent Interaction},
author = {David V. Pynadath and Ning Wang and Sreekar Kamireddy},
url = {http://dl.acm.org/citation.cfm?doid=3349537.3351905},
doi = {10.1145/3349537.3351905},
isbn = {978-1-4503-6922-0},
year = {2019},
date = {2019-10-01},
booktitle = {Proceedings of the 7th International Conference on Human-Agent Interaction - HAI '19},
pages = {171–178},
publisher = {ACM Press},
address = {Kyoto, Japan},
abstract = {Trust calibration is critical to the success of human-agent interaction (HAI). However, individual differences are ubiquitous in people’s trust relationships with autonomous systems. To assist its heterogeneous human teammates calibrate their trust in it, an agent must first dynamically model them as individuals, rather than communicating with them all in the same manner. It can then generate expectations of its teammates’ behavior and optimize its own communication based on the current state of the trust relationship it has with them. In this work, we examine how an agent can generate accurate expectations given observations of only the teammate’s trust-related behaviors (e.g., did the person follow or ignore its advice?). In addition to this limited input, we also seek a specific output: accurately predicting its human teammate’s future trust behavior (e.g., will the person follow or ignore my next suggestion?). In this investigation, we construct a model capable of generating such expectations using data gathered in a humansubject study of behavior in a simulated human-robot interaction (HRI) scenario. We first analyze the ability of measures from a presurvey on trust-related traits to accurately predict subsequent trust behaviors. However, as the interaction progresses, this effect is dwarfed by the direct experience. We therefore analyze the ability of sequences of prior behavior by the teammate to accurately predict subsequent trust behaviors. Such behavioral sequences have shown to be indicative of the subjective beliefs of other teammates, and we show here that they have a predictive power as well.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Trust calibration is critical to the success of human-agent interaction (HAI). However, individual differences are ubiquitous in people’s trust relationships with autonomous systems. To assist its heterogeneous human teammates calibrate their trust in it, an agent must first dynamically model them as individuals, rather than communicating with them all in the same manner. It can then generate expectations of its teammates’ behavior and optimize its own communication based on the current state of the trust relationship it has with them. In this work, we examine how an agent can generate accurate expectations given observations of only the teammate’s trust-related behaviors (e.g., did the person follow or ignore its advice?). In addition to this limited input, we also seek a specific output: accurately predicting its human teammate’s future trust behavior (e.g., will the person follow or ignore my next suggestion?). In this investigation, we construct a model capable of generating such expectations using data gathered in a humansubject study of behavior in a simulated human-robot interaction (HRI) scenario. We first analyze the ability of measures from a presurvey on trust-related traits to accurately predict subsequent trust behaviors. However, as the interaction progresses, this effect is dwarfed by the direct experience. We therefore analyze the ability of sequences of prior behavior by the teammate to accurately predict subsequent trust behaviors. Such behavioral sequences have shown to be indicative of the subjective beliefs of other teammates, and we show here that they have a predictive power as well.
23.
Zhu, Runhe; Becerik-Gerber, Burcin; Lucas, Gale; Southers, Erroll; Pynadath, David V
Information Requirements for Virtual Environments to Study Human-Building Interactions during Active Shooter Incidents Journal Article
In: Computing in Civil Engineering, pp. 8, 2019.
@article{zhu_information_2019,
title = {Information Requirements for Virtual Environments to Study Human-Building Interactions during Active Shooter Incidents},
author = {Runhe Zhu and Burcin Becerik-Gerber and Gale Lucas and Erroll Southers and David V Pynadath},
url = {https://ascelibrary.org/doi/10.1061/9780784482445.024},
doi = {10.1061/9780784482445.024},
year = {2019},
date = {2019-06-01},
journal = {Computing in Civil Engineering},
pages = {8},
abstract = {Active shooter incidents present an increasing American homeland security threat to public safety and human life. Several municipal law enforcement agencies have released building design guidelines intended to offer increased resilience and resistance to potential attacks. However, these design recommendations mainly focus on terrorist attacks, prioritizing the enhancement of building security, whereas their impact on safety during active shooter incidents, and corresponding human-building interactions (HBIs) that influence the outcomes (response performance), remain unclear. To respond to this research gap, virtual reality, with its ability to manipulate environmental variables and scenarios while providing safe non-invasive environments, could be a promising method to conduct human-subject studies in the context of active shooter incidents. In this paper, we identify the requirements for developing virtual environments that represent active shooter incidents in buildings to study HBIs and their impacts on the response performance. Key components constituting virtual environments were considered and presented. These include: (1) what types of buildings should be modeled in virtual environments; (2) how to select protective building design recommendations for active shooter incidents and model them in virtual environments; (3) what types of adversary and crowd behavior should be modeled; and (4) what types of interactions among participants, buildings, adversaries, and crowds should be included in virtual environments. Findings on the above key components were summarized to provide recommendations for future research directions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Active shooter incidents present an increasing American homeland security threat to public safety and human life. Several municipal law enforcement agencies have released building design guidelines intended to offer increased resilience and resistance to potential attacks. However, these design recommendations mainly focus on terrorist attacks, prioritizing the enhancement of building security, whereas their impact on safety during active shooter incidents, and corresponding human-building interactions (HBIs) that influence the outcomes (response performance), remain unclear. To respond to this research gap, virtual reality, with its ability to manipulate environmental variables and scenarios while providing safe non-invasive environments, could be a promising method to conduct human-subject studies in the context of active shooter incidents. In this paper, we identify the requirements for developing virtual environments that represent active shooter incidents in buildings to study HBIs and their impacts on the response performance. Key components constituting virtual environments were considered and presented. These include: (1) what types of buildings should be modeled in virtual environments; (2) how to select protective building design recommendations for active shooter incidents and model them in virtual environments; (3) what types of adversary and crowd behavior should be modeled; and (4) what types of interactions among participants, buildings, adversaries, and crowds should be included in virtual environments. Findings on the above key components were summarized to provide recommendations for future research directions.
24.
Wang, Ning; Schwartz, David; Lewine, Gabrielle; Shapiro, Ari; Feng, Andrew; Zhuang, Cindy
Addressing Sexist Attitudes on a College Campus through Virtual Role-Play with Digital Doppelgangers Proceedings Article
In: Proceedings of the 18th International Conference on Intelligent Virtual Agents - IVA '18, pp. 219–226, ACM Press, Sydney, NSW, Australia, 2018, ISBN: 978-1-4503-6013-5.
@inproceedings{wang_addressing_2018,
title = {Addressing Sexist Attitudes on a College Campus through Virtual Role-Play with Digital Doppelgangers},
author = {Ning Wang and David Schwartz and Gabrielle Lewine and Ari Shapiro and Andrew Feng and Cindy Zhuang},
url = {http://dl.acm.org/citation.cfm?doid=3267851.3267913},
doi = {10.1145/3267851.3267913},
isbn = {978-1-4503-6013-5},
year = {2018},
date = {2018-11-01},
booktitle = {Proceedings of the 18th International Conference on Intelligent Virtual Agents - IVA '18},
pages = {219–226},
publisher = {ACM Press},
address = {Sydney, NSW, Australia},
abstract = {Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. Digital doppelgangers possess great potential to serve as powerful models for behavioral change. An emerging technology, the Rapid Avatar Capture and Simulation (RACAS) system, enables low-cost and high-speed scanning of a human user and creation of a digital doppelganger that is a fully animatable virtual 3D model of the user. We designed a virtual role-playing game, DELTA, that implements a powerful cognitive dissonance-based paradigm for attitudinal and behavioral change, and integrated it with digital doppelgangers to influence a human user’s attitude towards sexism on college campuses. In this paper, we discuss the design and evaluation the RACAS system and the DELTA game-based environment. Results indicate the potential impact of the DELTA game-based environment in creating an immersive virtual experience for attitudinal change.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. Digital doppelgangers possess great potential to serve as powerful models for behavioral change. An emerging technology, the Rapid Avatar Capture and Simulation (RACAS) system, enables low-cost and high-speed scanning of a human user and creation of a digital doppelganger that is a fully animatable virtual 3D model of the user. We designed a virtual role-playing game, DELTA, that implements a powerful cognitive dissonance-based paradigm for attitudinal and behavioral change, and integrated it with digital doppelgangers to influence a human user’s attitude towards sexism on college campuses. In this paper, we discuss the design and evaluation the RACAS system and the DELTA game-based environment. Results indicate the potential impact of the DELTA game-based environment in creating an immersive virtual experience for attitudinal change.
25.
Pynadath, David V.; Wang, Ning; Rovira, Ericka; Barnes, Michael J.
Clustering Behavior to Recognize Subjective Beliefs in Human-Agent Teams Proceedings Article
In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1495–1503, International Foundation for Autonomous Agents and Multiagent Systems, Stockholm, Sweden, 2018.
@inproceedings{pynadath_clustering_2018,
title = {Clustering Behavior to Recognize Subjective Beliefs in Human-Agent Teams},
author = {David V. Pynadath and Ning Wang and Ericka Rovira and Michael J. Barnes},
url = {https://dl.acm.org/citation.cfm?id=3237923},
year = {2018},
date = {2018-07-01},
booktitle = {Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems},
pages = {1495–1503},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Stockholm, Sweden},
abstract = {Trust is critical to the success of human-agent teams, and a critical antecedents to trust is transparency. To best interact with human teammates, an agent explain itself so that they understand its decision-making process. However, individual differences among human teammates require that the agent dynamically adjust its explanation strategy based on their unobservable subjective beliefs. The agent must therefore recognize its teammates' subjective beliefs relevant to trust-building (e.g., their understanding of the agent's capabilities and process). We leverage a nonparametric method to enable an agent to use its history of prior interactions as a means for recognizing and predicting a new teammate's subjective beliefs. We first gather data combining observable behavior sequences with survey-based observations of typically unobservable perceptions. We then use a nearest-neighbor approach to identify the prior teammates most similar to the new one. We use these neighbors' responses to infer the likelihood of possible beliefs, as in collaborative filtering. The results provide insights into the types of beliefs that are easy (and hard) to infer from purely behavioral observations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Trust is critical to the success of human-agent teams, and a critical antecedents to trust is transparency. To best interact with human teammates, an agent explain itself so that they understand its decision-making process. However, individual differences among human teammates require that the agent dynamically adjust its explanation strategy based on their unobservable subjective beliefs. The agent must therefore recognize its teammates' subjective beliefs relevant to trust-building (e.g., their understanding of the agent's capabilities and process). We leverage a nonparametric method to enable an agent to use its history of prior interactions as a means for recognizing and predicting a new teammate's subjective beliefs. We first gather data combining observable behavior sequences with survey-based observations of typically unobservable perceptions. We then use a nearest-neighbor approach to identify the prior teammates most similar to the new one. We use these neighbors' responses to infer the likelihood of possible beliefs, as in collaborative filtering. The results provide insights into the types of beliefs that are easy (and hard) to infer from purely behavioral observations.
26.
Pynadath, David V.; Wang, Ning; Rovira, Ericka; Barnes, Michael J.
A Nearest-Neighbor Approach to Recognizing Subjective Beliefs in Human-Robot Interaction Proceedings Article
In: Proceedings of The AAAI Workshop on Plan, Activity, and Intent Recognition (PAIR), Association for the Advancement of Artificial Intelligence, London, UK, 2018.
@inproceedings{pynadath_nearest-neighbor_2018,
title = {A Nearest-Neighbor Approach to Recognizing Subjective Beliefs in Human-Robot Interaction},
author = {David V. Pynadath and Ning Wang and Ericka Rovira and Michael J. Barnes},
url = {https://aied2018.utscic.edu.au/proceedings/},
year = {2018},
date = {2018-06-01},
booktitle = {Proceedings of The AAAI Workshop on Plan, Activity, and Intent Recognition (PAIR)},
publisher = {Association for the Advancement of Artificial Intelligence},
address = {London, UK},
abstract = {Trust is critical to the success of human-robot interaction (HRI), and one of the critical antecedents to trust is transparency. To best interact with human teammates, a robot must be able to ensure that they understand its decision-making process. Recent work has developed automated explanation methods that can achieve this goal. However, individual differences among human teammates require that the robot dynamically adjust its explanation strategy based on their unobservable subjective beliefs. We therefore need methods by which a robot can recognize its teammates’ subjective beliefs relevant to trust-building (e.g., their understanding of the robot’s capabilities and process). We leverage a nonparametric method, common across many fields of artificial intelligence, to enable a robot to use its history of prior interactions as a means for recognizing and predicting a new teammate’s subjective beliefs. We first gather data combining observable behavior sequences with surveybased observations of typically unobservable subjective beliefs. We then use a nearest-neighbor approach to identify the prior teammates most similar to the new one. We use these neighbors to infer the likelihood of possible subjective beliefs, and the results provide insights into the types of subjective beliefs that are easy (and hard) to infer from purely behavioral observations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Trust is critical to the success of human-robot interaction (HRI), and one of the critical antecedents to trust is transparency. To best interact with human teammates, a robot must be able to ensure that they understand its decision-making process. Recent work has developed automated explanation methods that can achieve this goal. However, individual differences among human teammates require that the robot dynamically adjust its explanation strategy based on their unobservable subjective beliefs. We therefore need methods by which a robot can recognize its teammates’ subjective beliefs relevant to trust-building (e.g., their understanding of the robot’s capabilities and process). We leverage a nonparametric method, common across many fields of artificial intelligence, to enable a robot to use its history of prior interactions as a means for recognizing and predicting a new teammate’s subjective beliefs. We first gather data combining observable behavior sequences with surveybased observations of typically unobservable subjective beliefs. We then use a nearest-neighbor approach to identify the prior teammates most similar to the new one. We use these neighbors to infer the likelihood of possible subjective beliefs, and the results provide insights into the types of subjective beliefs that are easy (and hard) to infer from purely behavioral observations.
27.
Pynadath, David V.; Barnes, Michael J.; Wang, Ning; Chen, Jessie Y. C.
Transparency Communication for Machine Learning in Human-Automation Interaction Book Section
In: Human and Machine Learning, pp. 75–90, Springer International Publishing, Cham, Switzerland, 2018, ISBN: 978-3-319-90402-3 978-3-319-90403-0.
@incollection{pynadath_transparency_2018,
title = {Transparency Communication for Machine Learning in Human-Automation Interaction},
author = {David V. Pynadath and Michael J. Barnes and Ning Wang and Jessie Y. C. Chen},
url = {http://link.springer.com/10.1007/978-3-319-90403-0_5},
doi = {10.1007/978-3-319-90403-0_5},
isbn = {978-3-319-90402-3 978-3-319-90403-0},
year = {2018},
date = {2018-06-01},
booktitle = {Human and Machine Learning},
pages = {75–90},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {Technological advances offer the promise of autonomous systems to form human-machine teams that are more capable than their individual members. Understanding the inner workings of the autonomous systems, especially as machine-learning (ML) methods are being widely applied to the design of such systems, has become increasingly challenging for the humans working with them. The “black-box” nature of quantitative ML approaches poses an impediment to people’s situation awareness (SA) of these ML-based systems, often resulting in either disuse or over-reliance of autonomous systems employing such algorithms. Research in human-automation interaction has shown that transparency communication can improve teammates’ SA, foster the trust relationship, and boost the human-automation team’s performance. In this chapter, we will examine the implications of an agent transparency model for human interactions with ML-based agents using automated explanations. We will discuss the application of a particular ML method, reinforcement learning (RL), in Partially Observable Markov Decision Process (POMDP)-based agents, and the design of explanation algorithms for RL in POMDPs.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Technological advances offer the promise of autonomous systems to form human-machine teams that are more capable than their individual members. Understanding the inner workings of the autonomous systems, especially as machine-learning (ML) methods are being widely applied to the design of such systems, has become increasingly challenging for the humans working with them. The “black-box” nature of quantitative ML approaches poses an impediment to people’s situation awareness (SA) of these ML-based systems, often resulting in either disuse or over-reliance of autonomous systems employing such algorithms. Research in human-automation interaction has shown that transparency communication can improve teammates’ SA, foster the trust relationship, and boost the human-automation team’s performance. In this chapter, we will examine the implications of an agent transparency model for human interactions with ML-based agents using automated explanations. We will discuss the application of a particular ML method, reinforcement learning (RL), in Partially Observable Markov Decision Process (POMDP)-based agents, and the design of explanation algorithms for RL in POMDPs.
28.
Pynadath, David V; Wang, Ning; Yang, Richard
Simulating Collaborative Learning through Decision- Theoretic Agents Proceedings Article
In: Proceedings of the Assessment and Intervention during Team Tutoring Workshop, CEUR-WS.org, London, UK, 2018.
@inproceedings{pynadath_simulating_2018,
title = {Simulating Collaborative Learning through Decision- Theoretic Agents},
author = {David V Pynadath and Ning Wang and Richard Yang},
url = {http://ceur-ws.org/Vol-2153/paper5.pdf},
year = {2018},
date = {2018-06-01},
booktitle = {Proceedings of the Assessment and Intervention during Team Tutoring Workshop},
publisher = {CEUR-WS.org},
address = {London, UK},
abstract = {Simulation for team training has a long history of success in medical care and emergency response. In fields where individuals work together to make decisions and perform actions under extreme time pressure and risk (as in military teams), simulations offer safe and repeatable environments for teams to learn and practice without real-world consequences. In our team-based training simulation, we use intelligent agents to represent individual learners and to autonomously generate behavior while learning to perform a joint task. Our agents are built upon PsychSim, a social-simulation framework that uses decision theory to provide domain-independent, quantitative algorithms for representing and reasoning about uncertainty and conflicting goals. We present a collaborative learning testbed in which two PsychSim agents performed a joint “capture-the-flag” mission in the presence of an enemy agent. The testbed supports a reinforcement-learning capability that enables the agents to revise their decision-theoretic models based on their experiences in performing the target task. We can “train” these agents by having them repeatedly perform the task and refine their models through reinforcement learning. We can then “test” the agents by measuring their performance once their learning has converged to a final policy. Repeating this trainand-test cycle across different parameter settings (e.g., priority of individual vs. team goals) and learning configurations (e.g., train with the same teammate vs. train with different teammates) yields a reusable methodology for characterizing the learning outcomes and measuring the impact of such variations on training effectiveness.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Simulation for team training has a long history of success in medical care and emergency response. In fields where individuals work together to make decisions and perform actions under extreme time pressure and risk (as in military teams), simulations offer safe and repeatable environments for teams to learn and practice without real-world consequences. In our team-based training simulation, we use intelligent agents to represent individual learners and to autonomously generate behavior while learning to perform a joint task. Our agents are built upon PsychSim, a social-simulation framework that uses decision theory to provide domain-independent, quantitative algorithms for representing and reasoning about uncertainty and conflicting goals. We present a collaborative learning testbed in which two PsychSim agents performed a joint “capture-the-flag” mission in the presence of an enemy agent. The testbed supports a reinforcement-learning capability that enables the agents to revise their decision-theoretic models based on their experiences in performing the target task. We can “train” these agents by having them repeatedly perform the task and refine their models through reinforcement learning. We can then “test” the agents by measuring their performance once their learning has converged to a final policy. Repeating this trainand-test cycle across different parameter settings (e.g., priority of individual vs. team goals) and learning configurations (e.g., train with the same teammate vs. train with different teammates) yields a reusable methodology for characterizing the learning outcomes and measuring the impact of such variations on training effectiveness.
29.
Wang, Ning; Shapiro, Ari; Feng, Andrew; Zhuang, Cindy; Schwartz, David; Goldberg, Stephen L.
An Analysis of Student Belief and Behavior in Learning by Explaining to a Digital Doppelganger Proceedings Article
In: Proceedings of the AIED Workshop on Personalized Approaches in Learning Environments (PALE), pp. 256–264, Springer International Publishing, Cham, Switzerland, 2018, ISBN: 978-3-319-91463-3 978-3-319-91464-0.
@inproceedings{wang_analysis_2018,
title = {An Analysis of Student Belief and Behavior in Learning by Explaining to a Digital Doppelganger},
author = {Ning Wang and Ari Shapiro and Andrew Feng and Cindy Zhuang and David Schwartz and Stephen L. Goldberg},
url = {http://ceur-ws.org/Vol-2141/paper3.pdf},
doi = {10.1007/978-3-319-91464-0_25},
isbn = {978-3-319-91463-3 978-3-319-91464-0},
year = {2018},
date = {2018-06-01},
booktitle = {Proceedings of the AIED Workshop on Personalized Approaches in Learning Environments (PALE)},
volume = {10858},
pages = {256–264},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. Using a low-cost and high-speed computer graphics and character animation technology, we created digital doppelgangers of students and placed them in a learning-byexplaining task where they interacted with digital doppelgangers of themselves. We investigate the research question of how does increasing the similarity of the physical appearance between the agent and the student impact learning. This paper discusses the design and evaluation of a digital doppelganger as a virtual human listener in a learning-by-explaining paradigm. It presents an analysis of how students’ perceptions of the resemblance impact their learning experience and outcomes. The analysis and results offer insight into the promise and limitation of the application of this novel technology to pedagogical agents research.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. Using a low-cost and high-speed computer graphics and character animation technology, we created digital doppelgangers of students and placed them in a learning-byexplaining task where they interacted with digital doppelgangers of themselves. We investigate the research question of how does increasing the similarity of the physical appearance between the agent and the student impact learning. This paper discusses the design and evaluation of a digital doppelganger as a virtual human listener in a learning-by-explaining paradigm. It presents an analysis of how students’ perceptions of the resemblance impact their learning experience and outcomes. The analysis and results offer insight into the promise and limitation of the application of this novel technology to pedagogical agents research.
30.
Wang, Ning; Shapiro, Ari; Feng, Andrew; Zhuang, Cindy; Merchant, Chirag; Schwartz, David; Goldberg, Stephen L.
Learning by Explaining to a Digital Doppelganger Book Section
In: Intelligent Tutoring Systems, vol. 10858, pp. 256–264, Springer International Publishing, Cham, Switzerland, 2018, ISBN: 978-3-319-91463-3 978-3-319-91464-0.
@incollection{wang_learning_2018,
title = {Learning by Explaining to a Digital Doppelganger},
author = {Ning Wang and Ari Shapiro and Andrew Feng and Cindy Zhuang and Chirag Merchant and David Schwartz and Stephen L. Goldberg},
url = {http://link.springer.com/10.1007/978-3-319-91464-0_25},
doi = {10.1007/978-3-319-91464-0_25},
isbn = {978-3-319-91463-3 978-3-319-91464-0},
year = {2018},
date = {2018-05-01},
booktitle = {Intelligent Tutoring Systems},
volume = {10858},
pages = {256–264},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. An emerging computer animation technology makes the creation of digital doppelgangers an accessible reality. This allows researchers in pedagogical agents to explore previously unexplorable research questions, such as how does increasing the similarity in appearance between the agent and the student impact learning. This paper discusses the design and evaluation of a digital doppelganger as a virtual listener in a learning-by-explaining paradigm. Results offer insight into the promise and limitation of this novel technology.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. An emerging computer animation technology makes the creation of digital doppelgangers an accessible reality. This allows researchers in pedagogical agents to explore previously unexplorable research questions, such as how does increasing the similarity in appearance between the agent and the student impact learning. This paper discusses the design and evaluation of a digital doppelganger as a virtual listener in a learning-by-explaining paradigm. Results offer insight into the promise and limitation of this novel technology.
31.
Wang, Ning; Pynadath, David V.; Rovira, Ericka; Barnes, Michael J.; Hill, Susan G.
In: Persuasive Technology, vol. 10809, pp. 56–69, Springer International Publishing, Cham, Switzerland, 2018, ISBN: 978-3-319-78977-4 978-3-319-78978-1.
@incollection{wang_is_2018,
title = {Is It My Looks? Or Something I Said? The Impact of Explanations, Embodiment, and Expectations on Trust and Performance in Human-Robot Teams},
author = {Ning Wang and David V. Pynadath and Ericka Rovira and Michael J. Barnes and Susan G. Hill},
url = {http://link.springer.com/10.1007/978-3-319-78978-1_5},
doi = {10.1007/978-3-319-78978-1_5},
isbn = {978-3-319-78977-4 978-3-319-78978-1},
year = {2018},
date = {2018-04-01},
booktitle = {Persuasive Technology},
volume = {10809},
pages = {56–69},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {Trust is critical to the success of human-robot interaction. Research has shown that people will more accurately trust a robot if they have an accurate understanding of its decision-making process. The Partially Observable Markov Decision Process (POMDP) is one such decision-making process, but its quantitative reasoning is typically opaque to people. This lack of transparency is exacerbated when a robot can learn, making its decision making better, but also less predictable. Recent research has shown promise in calibrating human-robot trust by automatically generating explanations of POMDP-based decisions. In this work, we explore factors that can potentially interact with such explanations in influencing human decision-making in human-robot teams. We focus on explanations with quantitative expressions of uncertainty and experiment with common design factors of a robot: its embodiment and its communication strategy in case of an error. Results help us identify valuable properties and dynamics of the human-robot trust relationship.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Trust is critical to the success of human-robot interaction. Research has shown that people will more accurately trust a robot if they have an accurate understanding of its decision-making process. The Partially Observable Markov Decision Process (POMDP) is one such decision-making process, but its quantitative reasoning is typically opaque to people. This lack of transparency is exacerbated when a robot can learn, making its decision making better, but also less predictable. Recent research has shown promise in calibrating human-robot trust by automatically generating explanations of POMDP-based decisions. In this work, we explore factors that can potentially interact with such explanations in influencing human decision-making in human-robot teams. We focus on explanations with quantitative expressions of uncertainty and experiment with common design factors of a robot: its embodiment and its communication strategy in case of an error. Results help us identify valuable properties and dynamics of the human-robot trust relationship.
32.
Wang, Ning; Pynadath, David V.; Barnes, Michael J.; Hill, Susan G.
Comparing Two Automatically Generated Explanations on the Perception of a Robot Teammate Proceedings Article
In: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, ACM, Chicago, IL, 2018.
@inproceedings{wang_comparing_2018,
title = {Comparing Two Automatically Generated Explanations on the Perception of a Robot Teammate},
author = {Ning Wang and David V. Pynadath and Michael J. Barnes and Susan G. Hill},
url = {http://people.ict.usc.edu/ nwang/PDF/HRI-ERS-2018-Wang.pdf},
year = {2018},
date = {2018-03-01},
booktitle = {Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction},
publisher = {ACM},
address = {Chicago, IL},
abstract = {Trust is critical to the success of human-robot interaction (HRI). Research has shown that people will more accurately trust a robot if they have a more accurate understanding of its decisionmaking process. Recent research has shown promise in calibrating human-agent trust by automatically generating explanations of decision-making process such as POMDP-based ones. In this paper, we compare two automatically generated explanations, one with quantitative information on uncertainty and one based on sensor observations, and study the impact of such explanations on perception of a robot in human-robot team.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Trust is critical to the success of human-robot interaction (HRI). Research has shown that people will more accurately trust a robot if they have a more accurate understanding of its decisionmaking process. Recent research has shown promise in calibrating human-agent trust by automatically generating explanations of decision-making process such as POMDP-based ones. In this paper, we compare two automatically generated explanations, one with quantitative information on uncertainty and one based on sensor observations, and study the impact of such explanations on perception of a robot in human-robot team.
33.
Wang, Ning; Shapiro, Ari; Schwartz, David; Lewine, Gabrielle; Feng, Andrew Wei-Wen
Virtual Role-Play with Rapid Avatars Book Section
In: Intelligent Virtual Agents, vol. 10498, pp. 463–466, Springer International Publishing, Cham, Switzerland, 2017, ISBN: 978-3-319-67400-1 978-3-319-67401-8.
@incollection{wang_virtual_2017,
title = {Virtual Role-Play with Rapid Avatars},
author = {Ning Wang and Ari Shapiro and David Schwartz and Gabrielle Lewine and Andrew Wei-Wen Feng},
url = {http://link.springer.com/10.1007/978-3-319-67401-8_59},
isbn = {978-3-319-67400-1 978-3-319-67401-8},
year = {2017},
date = {2017-08-01},
booktitle = {Intelligent Virtual Agents},
volume = {10498},
pages = {463–466},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {Digital doppelgangers possess great potential to serve as powerful models for behavioral change. An emerging technology, the Rapid Avatar Capture and Simulation (RACAS), enables low-cost and high-speed scanning of a human user and creation of a digital doppelganger that is a fully animatable virtual 3D model of the user. We designed a virtual role-playing game, DELTA, with digital doppelgangers to influence a human user’s attitude to-wards sexism on college campuses. In this demonstration, we will showcase the RACAS system and the DELTA game.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Digital doppelgangers possess great potential to serve as powerful models for behavioral change. An emerging technology, the Rapid Avatar Capture and Simulation (RACAS), enables low-cost and high-speed scanning of a human user and creation of a digital doppelganger that is a fully animatable virtual 3D model of the user. We designed a virtual role-playing game, DELTA, with digital doppelgangers to influence a human user’s attitude to-wards sexism on college campuses. In this demonstration, we will showcase the RACAS system and the DELTA game.
34.
Wang, Ning; Pynadath, David V.; Hill, Susan G.; Merchant, Chirag
The Dynamics of Human-Agent Trust with POMDP-Generated Explanations Proceedings Article
In: Proceedings of the 17th International Conference on Intelligent Virtual Agents (IVA 2017), Springer International Publishing, Stockholm, Sweden, 2017, ISBN: 978-3-319-67400-1 978-3-319-67401-8.
@inproceedings{wang_dynamics_2017,
title = {The Dynamics of Human-Agent Trust with POMDP-Generated Explanations},
author = {Ning Wang and David V. Pynadath and Susan G. Hill and Chirag Merchant},
url = {https://link.springer.com/chapter/10.1007/978-3-319-67401-8_58},
isbn = {978-3-319-67400-1 978-3-319-67401-8},
year = {2017},
date = {2017-08-01},
booktitle = {Proceedings of the 17th International Conference on Intelligent Virtual Agents (IVA 2017)},
publisher = {Springer International Publishing},
address = {Stockholm, Sweden},
abstract = {Partially Observable Markov Decision Processes (POMDPs) enable optimized decision making by robots, agents, and other autonomous systems. This quantitative optimization can also be a limitation in human-agent interaction, as the resulting autonomous behavior, while possibly optimal, is often impenetrable to human teammates, leading to improper trust and, subsequently, disuse or misuse of such systems [1]. Automatically generated explanations of POMDP-based decisions have shown promise in calibrating human-agent trust [3]. However, these “one-size-fits-all” static explanation policies are insufficient to accommodate different communication preferences across people. In this work, we analyze human behavior in a human-robot interaction (HRI) scenario, to find behavioral indicators of trust in the agent’s ability. We evaluate four hypothesized behavioral measures that an agent could potentially use to dynamically infer its teammate’s current trust level. The conclusions drawn can potentially inform the design of intelligent agents that can automatically adapt their explanation policies as they observe the behavioral responses of their human teammates.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Partially Observable Markov Decision Processes (POMDPs) enable optimized decision making by robots, agents, and other autonomous systems. This quantitative optimization can also be a limitation in human-agent interaction, as the resulting autonomous behavior, while possibly optimal, is often impenetrable to human teammates, leading to improper trust and, subsequently, disuse or misuse of such systems [1]. Automatically generated explanations of POMDP-based decisions have shown promise in calibrating human-agent trust [3]. However, these “one-size-fits-all” static explanation policies are insufficient to accommodate different communication preferences across people. In this work, we analyze human behavior in a human-robot interaction (HRI) scenario, to find behavioral indicators of trust in the agent’s ability. We evaluate four hypothesized behavioral measures that an agent could potentially use to dynamically infer its teammate’s current trust level. The conclusions drawn can potentially inform the design of intelligent agents that can automatically adapt their explanation policies as they observe the behavioral responses of their human teammates.
35.
Jalal-Kamali, Ali; Pynadath, David V.
Toward a Bayesian Network Model of Events in International Relations Proceedings Article
In: Procedings of the 2016 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Springer, Washington D.C., 2016.
@inproceedings{jalal-kamali_toward_2016,
title = {Toward a Bayesian Network Model of Events in International Relations},
author = {Ali Jalal-Kamali and David V. Pynadath},
url = {https://books.google.com/books?id=_HGADAAAQBAJ&pg=PA321&lpg=PA321&dq=Toward+a+Bayesian+network+model+of+events+in+international+relations&source=bl&ots=JBOYm4KCF2&sig=eqmzgrWXwDroEtoLyxZxSjxDIAs&hl=en&sa=X&ved=0ahUKEwiIgoSS8o_PAhUUzGMKHWnaDlEQ6AEILjAC#v=onepage&q=Toward%20a%20Bayesian%20network%20model%20of%20events%20in%20international%20relations&f=false},
year = {2016},
date = {2016-07-01},
booktitle = {Procedings of the 2016 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation},
publisher = {Springer},
address = {Washington D.C.},
abstract = {Formal models of international relations have a long history of exploiting representations and algorithms from artificial intelligence. As more news sources move online, there is an increasing wealth of data that can inform the creation of such models. The Global Database of Events, Language, and Tone (GDELT) extracts events from news articles from around the world, where the events represent actions taken by geopolitical actors, reflecting the actors’ relationships. We can apply existing machine-learning algorithms to automatically construct a Bayesian network that represents the distribution over the actions between actors. Such a network model allows us to analyze the interdependencies among events and generate the relative likelihoods of different events. By examining the accuracy of the learned network over different years and different actor pairs, we are able to identify aspects of international relations from a data-driven approach.We are also able to identify weaknesses in the model that suggest needs for additional domain knowledge.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Formal models of international relations have a long history of exploiting representations and algorithms from artificial intelligence. As more news sources move online, there is an increasing wealth of data that can inform the creation of such models. The Global Database of Events, Language, and Tone (GDELT) extracts events from news articles from around the world, where the events represent actions taken by geopolitical actors, reflecting the actors’ relationships. We can apply existing machine-learning algorithms to automatically construct a Bayesian network that represents the distribution over the actions between actors. Such a network model allows us to analyze the interdependencies among events and generate the relative likelihoods of different events. By examining the accuracy of the learned network over different years and different actor pairs, we are able to identify aspects of international relations from a data-driven approach.We are also able to identify weaknesses in the model that suggest needs for additional domain knowledge.
36.
Nazari, Zahra; Gratch, Jonathan
Predictive Models of Malicious Behavior in Human Negotiations Journal Article
In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 855–861, 2016.
@article{nazari_predictive_2016,
title = {Predictive Models of Malicious Behavior in Human Negotiations},
author = {Zahra Nazari and Jonathan Gratch},
url = {http://www.ijcai.org/Proceedings/16/Papers/126.pdf},
year = {2016},
date = {2016-07-01},
journal = {Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence},
pages = {855–861},
abstract = {Human and artificial negotiators must exchange information to find efficient negotiated agreements, but malicious actors could use deception to gain unfair advantage. The misrepresentation game is a game-theoretic formulation of how deceptive actors could gain disproportionate rewards while seeming honest and fair. Previous research proposed a solution to this game but this required restrictive assumptions that might render it inapplicable to realworld settings. Here we evaluate the formalism against a large corpus of human face-to-face negotiations. We confirm that the model captures how dishonest human negotiators win while seeming fair, even in unstructured negotiations. We also show that deceptive negotiators give-off signals of their malicious behavior, providing the opportunity for algorithms to detect and defeat this malicious tactic.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human and artificial negotiators must exchange information to find efficient negotiated agreements, but malicious actors could use deception to gain unfair advantage. The misrepresentation game is a game-theoretic formulation of how deceptive actors could gain disproportionate rewards while seeming honest and fair. Previous research proposed a solution to this game but this required restrictive assumptions that might render it inapplicable to realworld settings. Here we evaluate the formalism against a large corpus of human face-to-face negotiations. We confirm that the model captures how dishonest human negotiators win while seeming fair, even in unstructured negotiations. We also show that deceptive negotiators give-off signals of their malicious behavior, providing the opportunity for algorithms to detect and defeat this malicious tactic.
37.
Wang, Ning; Pynadath, David V.; Hill, Susan G.
The Impact of POMDP-Generated Explanations on Trust and Performance in Human-Robot Teams Proceedings Article
In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pp. 997–1005, International Foundation for Autonomous Agents and Multiagent Systems, Singapore, 2016, ISBN: 978-1-4503-4239-1.
@inproceedings{wang_impact_2016,
title = {The Impact of POMDP-Generated Explanations on Trust and Performance in Human-Robot Teams},
author = {Ning Wang and David V. Pynadath and Susan G. Hill},
url = {http://dl.acm.org/citation.cfm?id=2937071},
isbn = {978-1-4503-4239-1},
year = {2016},
date = {2016-05-01},
booktitle = {Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems},
pages = {997–1005},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Singapore},
abstract = {Researchers have observed that people will more accurately trust an autonomous system, such as a robot, if they have a more accurate understanding of its decision-making process. Studies have shown that hand-crafted explanations can help maintain effective team performance even when the system is less than 100% reliable. However, current explanation algorithms are not sufficient for making a robot's quantitative reasoning (in terms of both uncertainty and conflicting goals) transparent to human teammates. In this work, we develop a novel mechanism for robots to automatically generate explanations of reasoning based on Partially Observable Markov Decision Problems (POMDPs). Within this mechanism, we implement alternate natural-language templates and then measure their differential impact on trust and team performance within an agent-based online test-bed that simulates a human-robot team task. The results demonstrate that the added explanation capability leads to improvement in transparency, trust, and team performance. Furthermore, by observing the different outcomes due to variations in the robot's explanation content, we gain valuable insight that can help lead to refinement of explanation algorithms to further improve human-robot interaction.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Researchers have observed that people will more accurately trust an autonomous system, such as a robot, if they have a more accurate understanding of its decision-making process. Studies have shown that hand-crafted explanations can help maintain effective team performance even when the system is less than 100% reliable. However, current explanation algorithms are not sufficient for making a robot's quantitative reasoning (in terms of both uncertainty and conflicting goals) transparent to human teammates. In this work, we develop a novel mechanism for robots to automatically generate explanations of reasoning based on Partially Observable Markov Decision Problems (POMDPs). Within this mechanism, we implement alternate natural-language templates and then measure their differential impact on trust and team performance within an agent-based online test-bed that simulates a human-robot team task. The results demonstrate that the added explanation capability leads to improvement in transparency, trust, and team performance. Furthermore, by observing the different outcomes due to variations in the robot's explanation content, we gain valuable insight that can help lead to refinement of explanation algorithms to further improve human-robot interaction.
38.
Pynadath, David V.; Rosoff, Heather; John, Richard S.
Semi-Automated Construction of Decision-Theoretic Models of Human Behavior Proceedings Article
In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pp. 891–899, International Foundation for Autonomous Agents and Multiagent Systems, Singapore, 2016, ISBN: 978-1-4503-4239-1.
@inproceedings{pynadath_semi-automated_2016,
title = {Semi-Automated Construction of Decision-Theoretic Models of Human Behavior},
author = {David V. Pynadath and Heather Rosoff and Richard S. John},
url = {http://dl.acm.org/citation.cfm?id=2937055},
isbn = {978-1-4503-4239-1},
year = {2016},
date = {2016-05-01},
booktitle = {Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems},
pages = {891–899},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Singapore},
abstract = {Multiagent social simulation provides a powerful mechanism for policy makers to understand the potential outcomes of their decisions before implementing them. However, the value of such simulations depends on the accuracy of their underlying agent models. In this work, we present a method for automatically exploring a space of decision-theoretic models to arrive at a multiagent social simulation that is consistent with human behavior data. We start with a factored Partially Observable Markov Decision Process (POMDP) whose states, actions, and reward capture the questions asked in a survey from a disaster response scenario. Using input from domain experts, we construct a set of hypothesized dependencies that may or may not exist in the transition probability function. We present an algorithm to search through each of these hypotheses, evaluate their accuracy with respect to the data, and choose the models that best re ect the observed behavior, including individual di⬚erences. The result is a mechanism for constructing agent models that are grounded in human behavior data, while still being able to support hypothetical reasoning that is the main advantage of multiagent social simulation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Multiagent social simulation provides a powerful mechanism for policy makers to understand the potential outcomes of their decisions before implementing them. However, the value of such simulations depends on the accuracy of their underlying agent models. In this work, we present a method for automatically exploring a space of decision-theoretic models to arrive at a multiagent social simulation that is consistent with human behavior data. We start with a factored Partially Observable Markov Decision Process (POMDP) whose states, actions, and reward capture the questions asked in a survey from a disaster response scenario. Using input from domain experts, we construct a set of hypothesized dependencies that may or may not exist in the transition probability function. We present an algorithm to search through each of these hypotheses, evaluate their accuracy with respect to the data, and choose the models that best re ect the observed behavior, including individual di⬚erences. The result is a mechanism for constructing agent models that are grounded in human behavior data, while still being able to support hypothetical reasoning that is the main advantage of multiagent social simulation.
39.
Gratch, Jonathan; Nazari, Zahra; Johnson, Emmanuel
The Misrepresentation Game: How to win at negotiation while seeming like a nice guy Proceedings Article
In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pp. 728–737, International Foundation for Autonomous Agents and Multiagent Systems, Singapore, 2016, ISBN: 978-1-4503-4239-1.
@inproceedings{gratch_misrepresentation_2016,
title = {The Misrepresentation Game: How to win at negotiation while seeming like a nice guy},
author = {Jonathan Gratch and Zahra Nazari and Emmanuel Johnson},
url = {http://dl.acm.org/citation.cfm?id=2937031},
isbn = {978-1-4503-4239-1},
year = {2016},
date = {2016-05-01},
booktitle = {Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems},
pages = {728–737},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Singapore},
abstract = {Recently, interest has grown in agents that negotiate with people: to teach negotiation, to negotiate on behalf of people, and as a chal-lenge problem to advance artificial social intelligence. Humans ne-gotiate differently from algorithmic approaches to negotiation: peo-ple are not purely self-interested but place considerable weight on norms like fairness; people exchange information about their men-tal state and use this to judge the fairness of a social exchange; and people lie. Here, we focus on lying. We present an analysis of how people (or agents interacting with people) might optimally lie (maximally benefit themselves) while maintaining the illusion of fairness towards the other party. In doing so, we build on concepts from game theory and the preference-elicitation literature, but ap-ply these to human, not rational, behavior. Our findings demon-strate clear benefits to lying and provide empirical support for a heuristic – the “fixed-pie lie” – that substantially enhances the effi-ciency of such deceptive algorithms. We conclude with implica-tions and potential defenses against such manipulative techniques.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recently, interest has grown in agents that negotiate with people: to teach negotiation, to negotiate on behalf of people, and as a chal-lenge problem to advance artificial social intelligence. Humans ne-gotiate differently from algorithmic approaches to negotiation: peo-ple are not purely self-interested but place considerable weight on norms like fairness; people exchange information about their men-tal state and use this to judge the fairness of a social exchange; and people lie. Here, we focus on lying. We present an analysis of how people (or agents interacting with people) might optimally lie (maximally benefit themselves) while maintaining the illusion of fairness towards the other party. In doing so, we build on concepts from game theory and the preference-elicitation literature, but ap-ply these to human, not rational, behavior. Our findings demon-strate clear benefits to lying and provide empirical support for a heuristic – the “fixed-pie lie” – that substantially enhances the effi-ciency of such deceptive algorithms. We conclude with implica-tions and potential defenses against such manipulative techniques.
40.
Georgila, Kallirroi; Pynadath, David V.
Towards a Computational Model of Human Opinion Dynamics in Response to Real-World Events Proceedings Article
In: Proceedings of The 29th International FLAIRS Conference, pp. 44–49, AAAI Press, Key Largo, FL, 2016.
@inproceedings{georgila_towards_2016,
title = {Towards a Computational Model of Human Opinion Dynamics in Response to Real-World Events},
author = {Kallirroi Georgila and David V. Pynadath},
url = {http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS16/paper/view/12960/12539},
year = {2016},
date = {2016-03-01},
booktitle = {Proceedings of The 29th International FLAIRS Conference},
pages = {44–49},
publisher = {AAAI Press},
address = {Key Largo, FL},
abstract = {Accurate multiagent social simulation requires a computational model of how people incorporate their observations of real-world events into their beliefs about the state of their world. Current methods for creating such agent-based models typically rely on manual input that can be both burdensome and subjective. In this investigation, we instead pursue automated methods that can translate available data into the desired computational models. For this purpose, we use a corpus of real-world events in combination with longitudinal public opinion polls on a variety of opinion issues. We perform two experiments using automated methods taken from the literature. In our first experiment, we train maximum entropy classifiers to model changes in opinion scores as a function of real-world events. We measure and analyze the accuracy of our learned classifiers by comparing the opinion scores they generate against the opinion scores occurring in a held-out subset of our corpus. In our second experiment, we learn Bayesian networks to capture the same function.We then compare the dependency structures induced by the two methods to identify the event features that have the most significant effect on changes in public opinion.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Accurate multiagent social simulation requires a computational model of how people incorporate their observations of real-world events into their beliefs about the state of their world. Current methods for creating such agent-based models typically rely on manual input that can be both burdensome and subjective. In this investigation, we instead pursue automated methods that can translate available data into the desired computational models. For this purpose, we use a corpus of real-world events in combination with longitudinal public opinion polls on a variety of opinion issues. We perform two experiments using automated methods taken from the literature. In our first experiment, we train maximum entropy classifiers to model changes in opinion scores as a function of real-world events. We measure and analyze the accuracy of our learned classifiers by comparing the opinion scores they generate against the opinion scores occurring in a held-out subset of our corpus. In our second experiment, we learn Bayesian networks to capture the same function.We then compare the dependency structures induced by the two methods to identify the event features that have the most significant effect on changes in public opinion.
Filter
2015
Wang, Ning; Pynadath, David V.; Marsella, Stacy C.
Subjective Perceptions in Wartime Negotiation Journal Article
In: IEEE Transactions on Affective Computing, vol. 6, no. 2, pp. 118–126, 2015, ISSN: 1949-3045.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC
@article{wang_subjective_2015,
title = {Subjective Perceptions in Wartime Negotiation},
author = {Ning Wang and David V. Pynadath and Stacy C. Marsella},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6975149},
doi = {10.1109/TAFFC.2014.2378312},
issn = {1949-3045},
year = {2015},
date = {2015-04-01},
journal = {IEEE Transactions on Affective Computing},
volume = {6},
number = {2},
pages = {118–126},
abstract = {The prevalence of negotiation in social interaction has motivated researchers to develop virtual agents that can understand, facilitate, teach and even carry out negotiations. While much of this research has analyzed how to maximize the objective outcome, there is a growing body of work demonstrating that subjective perceptions of the outcome also play a critical role in human negotiation behavior. People derive subjective value from not only the outcome, but also from the process by which they achieve that outcome, from their relationship with their negotiation partner, etc. The affective responses evoked by these subjective valuations can be very different from what would be evoked by the objective outcome alone. We investigate such subjective valuations within human-agent negotiation in four variations of a wartime negotiation game. We observe that the objective outcomes of these negotiations are not strongly correlated with the human negotiators’ subjective perceptions, as measured by the Subjective Value Index. We examine the game dynamics and agent behaviors to identify features that induce different subjective values in the participants. We thus are able to identify characteristics of the negotiation process and the agents’ behavior that most impact people’s subjective valuations in our wartime negotiation games.⬚},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {article}
}
The prevalence of negotiation in social interaction has motivated researchers to develop virtual agents that can understand, facilitate, teach and even carry out negotiations. While much of this research has analyzed how to maximize the objective outcome, there is a growing body of work demonstrating that subjective perceptions of the outcome also play a critical role in human negotiation behavior. People derive subjective value from not only the outcome, but also from the process by which they achieve that outcome, from their relationship with their negotiation partner, etc. The affective responses evoked by these subjective valuations can be very different from what would be evoked by the objective outcome alone. We investigate such subjective valuations within human-agent negotiation in four variations of a wartime negotiation game. We observe that the objective outcomes of these negotiations are not strongly correlated with the human negotiators’ subjective perceptions, as measured by the Subjective Value Index. We examine the game dynamics and agent behaviors to identify features that induce different subjective values in the participants. We thus are able to identify characteristics of the negotiation process and the agents’ behavior that most impact people’s subjective valuations in our wartime negotiation games.⬚
2014
Rizzo, Albert; Scherer, Stefan; DeVault, David; Gratch, Jonathan; Artstein, Ron; Hartholt, Arno; Lucas, Gale; Marsella, Stacy; Morbini, Fabrizio; Nazarian, Angela; Stratou, Giota; Traum, David; Wood, Rachel; Boberg, Jill; Morency, Louis-Philippe
Detection and Computational Analysis of Psychological Signals Using a Virtual Human Interviewing Agent Proceedings Article
In: Proceedings of ICDVRAT 2014, International Journal of Disability and Human Development, Gothenburg, Sweden, 2014.
Abstract | Links | BibTeX | Tags: MedVR, Social Simulation, UARC, Virtual Humans
@inproceedings{rizzo_detection_2014,
title = {Detection and Computational Analysis of Psychological Signals Using a Virtual Human Interviewing Agent},
author = {Albert Rizzo and Stefan Scherer and David DeVault and Jonathan Gratch and Ron Artstein and Arno Hartholt and Gale Lucas and Stacy Marsella and Fabrizio Morbini and Angela Nazarian and Giota Stratou and David Traum and Rachel Wood and Jill Boberg and Louis-Philippe Morency},
url = {http://ict.usc.edu/pubs/Detection%20and%20Computational%20Analysis%20of%20Psychological%20Signals%20Using%20a%20Virtual%20Human%20Interviewing%20Agent.pdf},
year = {2014},
date = {2014-12-01},
booktitle = {Proceedings of ICDVRAT 2014},
publisher = {International Journal of Disability and Human Development},
address = {Gothenburg, Sweden},
abstract = {It has long been recognized that facial expressions, body posture/gestures and vocal parameters play an important role in human communication and the implicit signalling of emotion. Recent advances in low cost computer vision and behavioral sensing technologies can now be applied to the process of making meaningful inferences as to user state when a person interacts with a computational device. Effective use of this additive information could serve to promote human interaction with virtual human (VH) agents that may enhance diagnostic assessment. This paper will focus on our current research in these areas within the DARPA-funded “Detection and Computational Analysis of Psychological Signals” project, with specific attention to the SimSensei application use case. SimSensei is a virtual human interaction platform that is able to sense and interpret real-time audiovisual behavioral signals from users interacting with the system. It is specifically designed for health care support and leverages years of virtual human research and development at USC-ICT. The platform enables an engaging face-to-face interaction where the virtual human automatically reacts to the state and inferred intent of the user through analysis of behavioral signals gleaned from facial expressions, body gestures and vocal parameters. Akin to how non-verbal behavioral signals have an impact on human to human interaction and communication, SimSensei aims to capture and infer from user non-verbal communication to improve engagement between a VH and a user. The system can also quantify and interpret sensed behavioral signals.},
keywords = {MedVR, Social Simulation, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
It has long been recognized that facial expressions, body posture/gestures and vocal parameters play an important role in human communication and the implicit signalling of emotion. Recent advances in low cost computer vision and behavioral sensing technologies can now be applied to the process of making meaningful inferences as to user state when a person interacts with a computational device. Effective use of this additive information could serve to promote human interaction with virtual human (VH) agents that may enhance diagnostic assessment. This paper will focus on our current research in these areas within the DARPA-funded “Detection and Computational Analysis of Psychological Signals” project, with specific attention to the SimSensei application use case. SimSensei is a virtual human interaction platform that is able to sense and interpret real-time audiovisual behavioral signals from users interacting with the system. It is specifically designed for health care support and leverages years of virtual human research and development at USC-ICT. The platform enables an engaging face-to-face interaction where the virtual human automatically reacts to the state and inferred intent of the user through analysis of behavioral signals gleaned from facial expressions, body gestures and vocal parameters. Akin to how non-verbal behavioral signals have an impact on human to human interaction and communication, SimSensei aims to capture and infer from user non-verbal communication to improve engagement between a VH and a user. The system can also quantify and interpret sensed behavioral signals.
Feng, Andrew; Lucas, Gale; Marsella, Stacy; Suma, Evan; Chiu, Chung-Cheng; Casas, Dan; Shapiro, Ari
Acting the Part: The Role of Gesture on Avatar Identity Proceedings Article
In: Proceedings of the Seventh International Conference on Motion in Games (MIG 2014), pp. 49–54, ACM Press, Playa Vista, CA, 2014, ISBN: 978-1-4503-2623-0.
Abstract | Links | BibTeX | Tags: MxR, Social Simulation, UARC, Virtual Humans
@inproceedings{feng_acting_2014,
title = {Acting the Part: The Role of Gesture on Avatar Identity},
author = {Andrew Feng and Gale Lucas and Stacy Marsella and Evan Suma and Chung-Cheng Chiu and Dan Casas and Ari Shapiro},
url = {http://dl.acm.org/citation.cfm?doid=2668064.2668102},
doi = {10.1145/2668064.2668102},
isbn = {978-1-4503-2623-0},
year = {2014},
date = {2014-11-01},
booktitle = {Proceedings of the Seventh International Conference on Motion in Games (MIG 2014)},
pages = {49–54},
publisher = {ACM Press},
address = {Playa Vista, CA},
abstract = {Recent advances in scanning technology have enabled the widespread capture of 3D character models based on human subjects. However, in order to generate a recognizable 3D avatar, the movement and behavior of the human subject should be captured and replicated as well. We present a method of generating a 3D model from a scan, as well as a method to incorporate a subjects style of gesturing into a 3D character. We present a study which shows that 3D characters that used the gestural style as their original human subjects were more recognizable as the original subject than those that don’t.},
keywords = {MxR, Social Simulation, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advances in scanning technology have enabled the widespread capture of 3D character models based on human subjects. However, in order to generate a recognizable 3D avatar, the movement and behavior of the human subject should be captured and replicated as well. We present a method of generating a 3D model from a scan, as well as a method to incorporate a subjects style of gesturing into a 3D character. We present a study which shows that 3D characters that used the gestural style as their original human subjects were more recognizable as the original subject than those that don’t.
Feng, Andrew; Shapiro, Ari; Lhommet, Margaux; Marsella, Stacy
Embodied Autonomous Agents Book Section
In: Handbook of Virtual Environments: Design, Implementation, and Applications, pp. 335–352, 2014.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC, Virtual Humans
@incollection{feng_embodied_2014,
title = {Embodied Autonomous Agents},
author = {Andrew Feng and Ari Shapiro and Margaux Lhommet and Stacy Marsella},
url = {http://books.google.com/books?hl=en&lr=&id=7zzSBQAAQBAJ&oi=fnd&pg=PP1&dq=+Handbook+of+Virtual+Environments&ots=Vx3ia0S2Uu&sig=LaVbSdoG3FahlbVYbuCxLmKgFIA#v=onepage&q=Handbook%20of%20Virtual%20Environments&f=false},
year = {2014},
date = {2014-09-01},
booktitle = {Handbook of Virtual Environments: Design, Implementation, and Applications},
pages = {335–352},
abstract = {Since the last decade, virtual environments have been extensively used for a wide range of application, from training systems to video games. Virtual humans are animated characters that are designed to populate these environments and to interact with the objects of the world as well as with the user. A virtual agent must perceive the world in which it exists, reason about those perceptions, and decide on how to act on them in pursuit of its own agenda.},
keywords = {Social Simulation, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
Since the last decade, virtual environments have been extensively used for a wide range of application, from training systems to video games. Virtual humans are animated characters that are designed to populate these environments and to interact with the objects of the world as well as with the user. A virtual agent must perceive the world in which it exists, reason about those perceptions, and decide on how to act on them in pursuit of its own agenda.
Xu, Yuyu; Pelachaud, Catherine; Marsella, Stacy
Compound Gesture Generation: A Model Based on Ideational Units Proceedings Article
In: Intelligent Virtual Agents, pp. 477–491, Springer, Boston, MA, 2014.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC, Virtual Humans
@inproceedings{xu_compound_2014,
title = {Compound Gesture Generation: A Model Based on Ideational Units},
author = {Yuyu Xu and Catherine Pelachaud and Stacy Marsella},
url = {http://ict.usc.edu/pubs/Compound%20Gesture%20Generation%20-%20A%20Model%20Based%20on%20Ideational%20Units.pdf},
year = {2014},
date = {2014-08-01},
booktitle = {Intelligent Virtual Agents},
pages = {477–491},
publisher = {Springer},
address = {Boston, MA},
abstract = {This work presents a hierarchical framework that generates continuous gesture animation performance for virtual characters. As opposed to approaches that focus more on realizing individual gesture, the focus of this work is on the relation between gestures as part of an overall gesture performance. Following Calbris’ work [3], our approach is to structure the performance around ideational units and determine gestural features within and across these ideational units. Furthermore, we use Calbris’ work on the relation between form and meaning in gesture to help inform how individual gesture’s expressivity is manipulated. Our framework takes in high level communicative function descriptions, generates behavior descriptions and realizes them using our character animation engine. We define the specifications for these different levels of descriptions. Finally, we show the general results as well as experiments illustrating the impacts of the key features.},
keywords = {Social Simulation, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
This work presents a hierarchical framework that generates continuous gesture animation performance for virtual characters. As opposed to approaches that focus more on realizing individual gesture, the focus of this work is on the relation between gestures as part of an overall gesture performance. Following Calbris’ work [3], our approach is to structure the performance around ideational units and determine gestural features within and across these ideational units. Furthermore, we use Calbris’ work on the relation between form and meaning in gesture to help inform how individual gesture’s expressivity is manipulated. Our framework takes in high level communicative function descriptions, generates behavior descriptions and realizes them using our character animation engine. We define the specifications for these different levels of descriptions. Finally, we show the general results as well as experiments illustrating the impacts of the key features.
Lhommet, Margot; Marsella, Stacy
Metaphoric Gestures: Towards Grounded Mental Spaces Proceedings Article
In: Intelligent Virtual Agents, pp. 264–274, Springer, Boston, MA, 2014.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC, Virtual Humans
@inproceedings{lhommet_metaphoric_2014,
title = {Metaphoric Gestures: Towards Grounded Mental Spaces},
author = {Margot Lhommet and Stacy Marsella},
url = {http://ict.usc.edu/pubs/Metaphoric%20Gestures%20-%20Towards%20Grounded%20Mental%20Spaces.pdf},
year = {2014},
date = {2014-08-01},
booktitle = {Intelligent Virtual Agents},
pages = {264–274},
publisher = {Springer},
address = {Boston, MA},
abstract = {Gestures are related to the mental states and unfolding processes of thought, reasoning and verbal language production. This is especially apparent in the case of metaphors and metaphoric gestures. For example, talking about the importance of an idea by calling it a big idea and gesturing to indicate that large size is a manifestation of the use of metaphors in language and gesture. We propose a computational model of the influence of conceptual metaphors on gestures that maps from mental state representations of ideas to their expression in concrete, physical metaphoric gestures. This model relies on conceptual primary metaphors to map the abstract elements of the mental space to concrete physical elements that can be conveyed with gestures.},
keywords = {Social Simulation, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Gestures are related to the mental states and unfolding processes of thought, reasoning and verbal language production. This is especially apparent in the case of metaphors and metaphoric gestures. For example, talking about the importance of an idea by calling it a big idea and gesturing to indicate that large size is a manifestation of the use of metaphors in language and gesture. We propose a computational model of the influence of conceptual metaphors on gestures that maps from mental state representations of ideas to their expression in concrete, physical metaphoric gestures. This model relies on conceptual primary metaphors to map the abstract elements of the mental space to concrete physical elements that can be conveyed with gestures.
Pynadath, David V.; Rosenbloom, Paul S.; Marsella, Stacy C.
Reinforcement Learning for Adaptive Theory of Mind in the Sigma Cognitive Architecture Proceedings Article
In: Proceedings of the 7th Annual Conference on Artificial General Intelligence, pp. 143 – 154, Springer International Publishing, Quebec City, Canada, 2014, ISBN: 978-3-319-09273-7.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC, Virtual Humans
@inproceedings{pynadath_reinforcement_2014,
title = {Reinforcement Learning for Adaptive Theory of Mind in the Sigma Cognitive Architecture},
author = {David V. Pynadath and Paul S. Rosenbloom and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Reinforcement%20learning%20for%20adaptive%20Theory%20of%20Mind%20in%20the%20Sigma%20cognitive%20architecture.pdf},
doi = {10.1007/978-3-319-09274-4_14},
isbn = {978-3-319-09273-7},
year = {2014},
date = {2014-08-01},
booktitle = {Proceedings of the 7th Annual Conference on Artificial General Intelligence},
pages = {143 – 154},
publisher = {Springer International Publishing},
address = {Quebec City, Canada},
abstract = {One of the most common applications of human intelligence is social interaction, where people must make effective decisions despite uncertainty about the potential behavior of others around them. Reinforcement learning (RL) provides one method for agents to acquire knowledge about such interactions. We investigate different methods of multiagent reinforcement learning within the Sigma cognitive architecture. We leverage Sigma’s architectural mechanism for gradient descent to realize four different approaches to multiagent learning: (1) with no explicit model of the other agent, (2) with a model of the other agent as following an unknown stationary policy, (3) with prior knowledge of the other agent’s possible reward functions, and (4) through inverse reinforcement learn- ing (IRL) of the other agent’s reward function. While the first three variations re-create existing approaches from the literature, the fourth represents a novel combination of RL and IRL for social decision-making. We show how all four styles of adaptive Theory of Mind are realized through Sigma’s same gradient descent algorithm, and we illustrate their behavior within an abstract negotiation task.},
keywords = {Social Simulation, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most common applications of human intelligence is social interaction, where people must make effective decisions despite uncertainty about the potential behavior of others around them. Reinforcement learning (RL) provides one method for agents to acquire knowledge about such interactions. We investigate different methods of multiagent reinforcement learning within the Sigma cognitive architecture. We leverage Sigma’s architectural mechanism for gradient descent to realize four different approaches to multiagent learning: (1) with no explicit model of the other agent, (2) with a model of the other agent as following an unknown stationary policy, (3) with prior knowledge of the other agent’s possible reward functions, and (4) through inverse reinforcement learn- ing (IRL) of the other agent’s reward function. While the first three variations re-create existing approaches from the literature, the fourth represents a novel combination of RL and IRL for social decision-making. We show how all four styles of adaptive Theory of Mind are realized through Sigma’s same gradient descent algorithm, and we illustrate their behavior within an abstract negotiation task.
McAlinden, Ryan; Pynadath, David V.; Hill, Randall W.
UrbanSim: Using Social Simulation to Train for Stability Operations Book Section
In: Understanding Megacities with the Reconnaissance, Surveillance, and Intelligence Paradigm, 2014.
Abstract | Links | BibTeX | Tags: Social Simulation, STG, UARC
@incollection{mcalinden_urbansim_2014,
title = {UrbanSim: Using Social Simulation to Train for Stability Operations},
author = {Ryan McAlinden and David V. Pynadath and Randall W. Hill},
url = {http://ict.usc.edu/pubs/UrbanSim%20-%20Using%20Social%20Simulation%20to%20Train%20for%20Stability%20Operations.pdf},
year = {2014},
date = {2014-04-01},
booktitle = {Understanding Megacities with the Reconnaissance, Surveillance, and Intelligence Paradigm},
abstract = {As the United States reorients itself towards to a period of reduced military capacity and away from large‐footprint military engagements, there is an imperative to keep commanders and decision‐makers mentally sharp and prepared for the next ‘hot spot.’ One potential hot spot, megacities, presents a unique set of challenges due to their expansive, often interwoven ethnographic landscapes, and their overall lack of understanding by many western experts. Social simulation using agent‐based models is one approach for furthering our understanding of distant societies and their security implications, and for preparing leaders to engage these populations if and when the need arises. Over the past ten years, the field of social simulation has become decidedly cross‐discipline, including academics and practitioners from the fields of sociology, anthropology, psychology, artificial intelligence and engineering. This has led to an unparalleled advancement in social simulation theory and practice, and as new threats evolve to operate within dense but expansive urban environments, social simulation has a unique opportunity to shape our perspectives and develop knowledge that may otherwise be difficult to obtain. This article presents a social simulation‐based training application (UrbanSim) developed by the University of Southern California’s Institute for Creative Technologies (USC‐ICT) in partnership with the US Army’s School for Command Preparation (SCP). UrbanSim has been in‐use since 2009 to help Army commanders understand and train for missions in complex, uncertain environments. The discussion describes how the social simulation‐based training application was designed to develop and hone commanders' skills for conducting missions in environs with multifaceted social, ethnic and political fabrics. We present a few considerations when attempting to recreate dense, rapidly growing population centers, and how the integration of real‐world data into social simulation frameworks can add a level of realism and understanding not possible even a few years ago.},
keywords = {Social Simulation, STG, UARC},
pubstate = {published},
tppubtype = {incollection}
}
As the United States reorients itself towards to a period of reduced military capacity and away from large‐footprint military engagements, there is an imperative to keep commanders and decision‐makers mentally sharp and prepared for the next ‘hot spot.’ One potential hot spot, megacities, presents a unique set of challenges due to their expansive, often interwoven ethnographic landscapes, and their overall lack of understanding by many western experts. Social simulation using agent‐based models is one approach for furthering our understanding of distant societies and their security implications, and for preparing leaders to engage these populations if and when the need arises. Over the past ten years, the field of social simulation has become decidedly cross‐discipline, including academics and practitioners from the fields of sociology, anthropology, psychology, artificial intelligence and engineering. This has led to an unparalleled advancement in social simulation theory and practice, and as new threats evolve to operate within dense but expansive urban environments, social simulation has a unique opportunity to shape our perspectives and develop knowledge that may otherwise be difficult to obtain. This article presents a social simulation‐based training application (UrbanSim) developed by the University of Southern California’s Institute for Creative Technologies (USC‐ICT) in partnership with the US Army’s School for Command Preparation (SCP). UrbanSim has been in‐use since 2009 to help Army commanders understand and train for missions in complex, uncertain environments. The discussion describes how the social simulation‐based training application was designed to develop and hone commanders' skills for conducting missions in environs with multifaceted social, ethnic and political fabrics. We present a few considerations when attempting to recreate dense, rapidly growing population centers, and how the integration of real‐world data into social simulation frameworks can add a level of realism and understanding not possible even a few years ago.
Sukthankar, Gita; Goldman, Robert P.; Geib, Christopher; Pynadath, David V.; Bui, Hung
Plan, Activity, and Intent Recognition: Theory and Practice Book
Morgan Kaufmann, 2014, ISBN: 0-12-398532-3.
Links | BibTeX | Tags: Social Simulation, UARC
@book{sukthankar_plan_2014,
title = {Plan, Activity, and Intent Recognition: Theory and Practice},
author = {Gita Sukthankar and Robert P. Goldman and Christopher Geib and David V. Pynadath and Hung Bui},
url = {http://www.amazon.com/Plan-Activity-Intent-Recognition-Practice/dp/0123985323/ref=sr_1_1?s=books&ie=UTF8&qid=1408747877&sr=1-1&keywords=Plan%2C+Activity%2C+and+Intent+Recognition%3A+Theory+and+Practice},
isbn = {0-12-398532-3},
year = {2014},
date = {2014-03-01},
publisher = {Morgan Kaufmann},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {book}
}
Hill, Randall W.
Virtual Reality and Leadership Development Book Section
In: Using Experience to Develop Leadership Talent: How Organizations Leverage On-The-Job Development, pp. 286–312, John Wiley & Sons, Inc., 2014, ISBN: 978-1-118-76783-2.
Links | BibTeX | Tags: Learning Sciences, Social Simulation, UARC, Virtual Humans, Virtual Worlds
@incollection{hill_virtual_2014,
title = {Virtual Reality and Leadership Development},
author = {Randall W. Hill},
url = {http://www.amazon.com/dp/1118767837/ref=cm_sw_su_dp},
isbn = {978-1-118-76783-2},
year = {2014},
date = {2014-03-01},
booktitle = {Using Experience to Develop Leadership Talent: How Organizations Leverage On-The-Job Development},
pages = {286–312},
publisher = {John Wiley & Sons, Inc.},
series = {J-B SIOP Professional Practice Series (Book 1)},
keywords = {Learning Sciences, Social Simulation, UARC, Virtual Humans, Virtual Worlds},
pubstate = {published},
tppubtype = {incollection}
}
2013
Ito, Jonathan Y.; Marsella, Stacy C.
Modeling Framing Effects Comparing an Appraisal-Based Model with Existing Models Proceedings Article
In: ACII 2013, pp. 381–386, IEEE Computer Society, 2013.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC
@inproceedings{ito_modeling_2013,
title = {Modeling Framing Effects Comparing an Appraisal-Based Model with Existing Models},
author = {Jonathan Y. Ito and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Modeling%20Framing%20Effects%20Comparing%20an%20Appraisal-Based%20Model%20with%20Existing%20Models.pdf},
year = {2013},
date = {2013-09-01},
booktitle = {ACII 2013},
pages = {381–386},
publisher = {IEEE Computer Society},
abstract = {One significant challenge in creating accurate models of human decision behavior is accounting for the effects of context. Research shows that seemingly minor changes in the presentation of a decision can lead to shifts in behavior; phenomena collectively referred to as framing effects. This work presents a computational modeling analysis comparing the effectiveness of Context Dependent Utility, an appraisal-based approach to modeling the multi-dimensional effects of context on decision behavior, against Cumulative Prospect Theory, Security-Potential/Aspiration Theory, the Transfer of Attention Exchange model, and a power-based utility function. To contrast model performance, a non-linear least-squares analysis and subsequent calculation of Akaike Information Criterion scores, which take into account goodness of fit while penalizing for model complexity, are employed. Results suggest that multi-dimensional models of context and framing, such as Context Dependent Utility, can be much more accurate in modeling decisions which similarly involve multi-dimensional considerations of context. Furthermore, this work demonstrates the effectiveness of employing affective constructs, such as appraisal, for encoding and evaluation of context within decision-theoretic frameworks to better model and predict human decision behavior.},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
One significant challenge in creating accurate models of human decision behavior is accounting for the effects of context. Research shows that seemingly minor changes in the presentation of a decision can lead to shifts in behavior; phenomena collectively referred to as framing effects. This work presents a computational modeling analysis comparing the effectiveness of Context Dependent Utility, an appraisal-based approach to modeling the multi-dimensional effects of context on decision behavior, against Cumulative Prospect Theory, Security-Potential/Aspiration Theory, the Transfer of Attention Exchange model, and a power-based utility function. To contrast model performance, a non-linear least-squares analysis and subsequent calculation of Akaike Information Criterion scores, which take into account goodness of fit while penalizing for model complexity, are employed. Results suggest that multi-dimensional models of context and framing, such as Context Dependent Utility, can be much more accurate in modeling decisions which similarly involve multi-dimensional considerations of context. Furthermore, this work demonstrates the effectiveness of employing affective constructs, such as appraisal, for encoding and evaluation of context within decision-theoretic frameworks to better model and predict human decision behavior.
Wang, Ning; Pynadath, David V.; Marsella, Stacy C.
Subjective Perceptions in Wartime Negotiation Proceedings Article
In: International Conference on Affective Computing and Intelligent Interaction, pp. 540 –545, Geneva, Switzerland, 2013.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC
@inproceedings{wang_subjective_2013,
title = {Subjective Perceptions in Wartime Negotiation},
author = {Ning Wang and David V. Pynadath and Stacy C. Marsella},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6681486&tag=1},
doi = {10.1109/ACII.2013.95},
year = {2013},
date = {2013-09-01},
booktitle = {International Conference on Affective Computing and Intelligent Interaction},
pages = {540 –545},
address = {Geneva, Switzerland},
abstract = {The prevalence of negotiation in social interaction has motivated researchers to develop virtual agents that can understand, facilitate, teach and even carry out negotiations. While much of this research has analyzed how to maximize the objective outcome, there is a growing body of work demonstrating that subjective perceptions of the outcome also play a critical role in human negotiation behavior. People derive subjective value from not only the outcome, but also from the process by which they achieve that outcome, from their relationship with their negotiation partner, etc. The affective responses evoked by these subjective valuations can be very different from what would be evoked by the objective outcome alone. We investigate such subjective valuations within human-agent negotiation in four variations of a wartime negotiation game. We observe that the objective outcomes of these negotiations are not strongly correlated with the human negotiators’ subjective perceptions, as measured by the Subjective Value Index. We examine the game dynamics and agent behaviors to identify features that induce different subjective values in the participants. We thus are able to identify characteristics of the negotiation process and the agents’ behavior that most impact people’s subjective valuations in our wartime negotiation games.⬚},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
The prevalence of negotiation in social interaction has motivated researchers to develop virtual agents that can understand, facilitate, teach and even carry out negotiations. While much of this research has analyzed how to maximize the objective outcome, there is a growing body of work demonstrating that subjective perceptions of the outcome also play a critical role in human negotiation behavior. People derive subjective value from not only the outcome, but also from the process by which they achieve that outcome, from their relationship with their negotiation partner, etc. The affective responses evoked by these subjective valuations can be very different from what would be evoked by the objective outcome alone. We investigate such subjective valuations within human-agent negotiation in four variations of a wartime negotiation game. We observe that the objective outcomes of these negotiations are not strongly correlated with the human negotiators’ subjective perceptions, as measured by the Subjective Value Index. We examine the game dynamics and agent behaviors to identify features that induce different subjective values in the participants. We thus are able to identify characteristics of the negotiation process and the agents’ behavior that most impact people’s subjective valuations in our wartime negotiation games.⬚
Pynadath, David V.; Wang, Ning; Marsella, Stacy C.
Computational Models of Human Behavior in Wartime Negotiations Proceedings Article
In: Annual Meeting of the Cognitive Science Society, Berlin, Germany, 2013.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC
@inproceedings{pynadath_computational_2013,
title = {Computational Models of Human Behavior in Wartime Negotiations},
author = {David V. Pynadath and Ning Wang and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Computational%20Models%20of%20Human%20Behavior%20in%20Wartime%20Negotiations.pdf},
year = {2013},
date = {2013-08-01},
booktitle = {Annual Meeting of the Cognitive Science Society},
address = {Berlin, Germany},
abstract = {Political scientists are increasingly turning to game-theoretic models to understand and predict the behavior of national leaders in wartime scenarios, where two sides have the options of seeking resolution at either the bargaining table or on the battlefield. While the theoretical analyses of these models is suggestive of their ability to capture these scenarios, it is not clear to what degree human behavior conforms to such equilibrium-based expectations. We present the results of a study that placed people within two of these game models, playing against an intelligent agent. We consider several testable hypotheses drawn from the theoretical analyses and evaluate the degree to which the observed human decisionmaking conforms to those hypotheses.⬚⬚⬚⬚},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Political scientists are increasingly turning to game-theoretic models to understand and predict the behavior of national leaders in wartime scenarios, where two sides have the options of seeking resolution at either the bargaining table or on the battlefield. While the theoretical analyses of these models is suggestive of their ability to capture these scenarios, it is not clear to what degree human behavior conforms to such equilibrium-based expectations. We present the results of a study that placed people within two of these game models, playing against an intelligent agent. We consider several testable hypotheses drawn from the theoretical analyses and evaluate the degree to which the observed human decisionmaking conforms to those hypotheses.⬚⬚⬚⬚
Ito, Jonathan Y.; Marsella, Stacy C.
Context Dependent Utility: Modeling Decision Behavior Across Contexts Proceedings Article
In: Annual Meeting of the Cognitive Science Society, Berlin, Germany, 2013.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC
@inproceedings{ito_context_2013,
title = {Context Dependent Utility: Modeling Decision Behavior Across Contexts},
author = {Jonathan Y. Ito and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Context%20Dependent%20Utility-%20Modeling%20Decision%20Behavior%20Across%20Contexts.pdf},
year = {2013},
date = {2013-08-01},
booktitle = {Annual Meeting of the Cognitive Science Society},
address = {Berlin, Germany},
abstract = {One significant challenge in creating accurate models of human decision behavior is accounting for the effect of context. Research shows that seemingly minor changes in the presentation of a decision can lead to drastic shifts in behavior; phenomena collectively referred to as framing effects. Previous work has developed Context Dependent Utility (CDU), a framework integrating Appraisal Theory with decision-theoretic principles. This work extends existing research by presenting a study exploring the behavioral predictions offered by CDU regarding the multidimensional effect of context on decision behavior. The present study finds support for the predictions of CDU regarding the impact of context on decisions: 1) as perceptions of pleasantness increase, decision behavior tends towards riskaversion; 2) as perceptions of goal-congruence increase, decision behavior tends towards risk-aversion; 3) as perceptions of controllability increase, i.e., perceptions that outcomes would have been primarily caused by the decision maker, behavior tends towards risk-seeking.⬚⬚⬚⬚⬚⬚⬚⬚⬚⬚},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
One significant challenge in creating accurate models of human decision behavior is accounting for the effect of context. Research shows that seemingly minor changes in the presentation of a decision can lead to drastic shifts in behavior; phenomena collectively referred to as framing effects. Previous work has developed Context Dependent Utility (CDU), a framework integrating Appraisal Theory with decision-theoretic principles. This work extends existing research by presenting a study exploring the behavioral predictions offered by CDU regarding the multidimensional effect of context on decision behavior. The present study finds support for the predictions of CDU regarding the impact of context on decisions: 1) as perceptions of pleasantness increase, decision behavior tends towards riskaversion; 2) as perceptions of goal-congruence increase, decision behavior tends towards risk-aversion; 3) as perceptions of controllability increase, i.e., perceptions that outcomes would have been primarily caused by the decision maker, behavior tends towards risk-seeking.⬚⬚⬚⬚⬚⬚⬚⬚⬚⬚
Pynadath, David V.; Wang, Ning; Marsella, Stacy C.
Are you thinking what I'm thinking? An Evaluation of Simplified Theory of Mind Proceedings Article
In: International Conference on Intelligent Virtual Humans, Edinburgh, UK, 2013.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC
@inproceedings{pynadath_are_2013,
title = {Are you thinking what I'm thinking? An Evaluation of Simplified Theory of Mind},
author = {David V. Pynadath and Ning Wang and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Are%20you%20thinking%20what%20Im%20thinking%20An%20Evaluation%20of%20Simplified%20Theory%20of%20Mind.pdf},
year = {2013},
date = {2013-08-01},
booktitle = {International Conference on Intelligent Virtual Humans},
address = {Edinburgh, UK},
abstract = {We examine the effectiveness of an agent's approximate theory of mind when interacting with human players in a wartime negotitation game. We first measure how accurately the agent's theory of mind captured the players' actual behavior. We observe significant overlap between the players' behavior and the agents' idealized expectations, but we also observe significant deviations. Forming an incorrect expectation about a person is not inherently damaging, so we then analyzed how different deviations affected the game outcomes. We observe that many classes of inaccuracy in the agent's theory of mind did not hurt the agent's performance and, in fact, some of them played to the agent's benefit. The results suggest potential advantages to giving an agent a computational model of theory of mind that is overly simplified, especially as a first step when investigating a domain with as much uncertainty as a wartime negotation.},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
We examine the effectiveness of an agent's approximate theory of mind when interacting with human players in a wartime negotitation game. We first measure how accurately the agent's theory of mind captured the players' actual behavior. We observe significant overlap between the players' behavior and the agents' idealized expectations, but we also observe significant deviations. Forming an incorrect expectation about a person is not inherently damaging, so we then analyzed how different deviations affected the game outcomes. We observe that many classes of inaccuracy in the agent's theory of mind did not hurt the agent's performance and, in fact, some of them played to the agent's benefit. The results suggest potential advantages to giving an agent a computational model of theory of mind that is overly simplified, especially as a first step when investigating a domain with as much uncertainty as a wartime negotation.
Lanquepin, Vincent; Lourdeaux, Domitile; Barot, Camille; Carpentier, Paul; Lhommet, Margaux; Amokrane, Kahina
HUMANS: a HUman Models Based Artificial eNvironments Software Platform Proceedings Article
In: Virtual Reality International Conference: Laval Virtual, ACM, Laval, France, 2013.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{lanquepin_humans_2013,
title = {HUMANS: a HUman Models Based Artificial eNvironments Software Platform},
author = {Vincent Lanquepin and Domitile Lourdeaux and Camille Barot and Paul Carpentier and Margaux Lhommet and Kahina Amokrane},
url = {http://dl.acm.org/citation.cfm?id=2466826},
year = {2013},
date = {2013-03-01},
booktitle = {Virtual Reality International Conference: Laval Virtual},
publisher = {ACM},
address = {Laval, France},
abstract = {Taking human-factors into account in training simulations enables these systems to address issues such as coactivity and management training. However, systems which use virtual reality technologies are usually designed so as to immerse the users in perfectly realistic virtual environment, focusing only on technical gestures and prescribed procedures. Therefore, they can only tackle situations with little complexity, where the user’s activity is highly constrained; otherwise they can’t ensure the pedagogic control and the relevance of the simulation. The HUMANS (HUman Models based Artificial eNvironments Software) platform is a generic framework, designed to build tailor-made virtual environments, which can be adapted to different application cases, technological configurations or pedagogical strategies. This suite rests upon the integration of multiple explicit models (domain, activity and risk model). In order to build ecologically valid virtual environments, these models represent not only the prescribed activity but the situated knowledge of operators about their tasks, including deviations from the procedures. Moreover, rather than a fixed world only populated by reactive characters, they are used to build a dynamic world populated with autonomous characters. These models can be used both by domain and procedures experts, and by computer experts. They are used both: to monitor learners actions, detecting errors and compromises; and to generate virtual characters behaviours.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Taking human-factors into account in training simulations enables these systems to address issues such as coactivity and management training. However, systems which use virtual reality technologies are usually designed so as to immerse the users in perfectly realistic virtual environment, focusing only on technical gestures and prescribed procedures. Therefore, they can only tackle situations with little complexity, where the user’s activity is highly constrained; otherwise they can’t ensure the pedagogic control and the relevance of the simulation. The HUMANS (HUman Models based Artificial eNvironments Software) platform is a generic framework, designed to build tailor-made virtual environments, which can be adapted to different application cases, technological configurations or pedagogical strategies. This suite rests upon the integration of multiple explicit models (domain, activity and risk model). In order to build ecologically valid virtual environments, these models represent not only the prescribed activity but the situated knowledge of operators about their tasks, including deviations from the procedures. Moreover, rather than a fixed world only populated by reactive characters, they are used to build a dynamic world populated with autonomous characters. These models can be used both by domain and procedures experts, and by computer experts. They are used both: to monitor learners actions, detecting errors and compromises; and to generate virtual characters behaviours.
Tsai, Jason; Bowring, Emma; Marsella, Stacy C.; Tambe, Milind
Empirical evaluation of computational fear contagion models in crowd dispersions Journal Article
In: Journal Autonomous Agents and Multi-Agent Systems, 2013.
Abstract | Links | BibTeX | Tags: Social Simulation, UARC
@article{tsai_empirical_2013,
title = {Empirical evaluation of computational fear contagion models in crowd dispersions},
author = {Jason Tsai and Emma Bowring and Stacy C. Marsella and Milind Tambe},
url = {http://ict.usc.edu/pubs/Empirical%20evaluation%20of%20computational%20fear%20contagion%20models%20in%20crowd%20dispersions.pdf},
year = {2013},
date = {2013-02-01},
journal = {Journal Autonomous Agents and Multi-Agent Systems},
abstract = {In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions.While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) and Durupinar, From audiences to mobs: Crowd simulation with psychological factors, PhD dissertation, Bilkent University, 2010) that draw from two separate lines of contagion research: thermodynamics-based and epidemiological-based. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.},
keywords = {Social Simulation, UARC},
pubstate = {published},
tppubtype = {article}
}
In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions.While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) and Durupinar, From audiences to mobs: Crowd simulation with psychological factors, PhD dissertation, Bilkent University, 2010) that draw from two separate lines of contagion research: thermodynamics-based and epidemiological-based. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.
2012
Tsai, Jason; Bowring, Emma; Marsella, Stacy C.; Wood, Wendy; Tambe, Milind
A Study of Emotional Contagion with Virtual Characters Proceedings Article
In: The 12th International Conference on Intelligent Virtual Agents (IVA), Santa Cruz, CA, 2012.
Abstract | Links | BibTeX | Tags: Social Simulation, Virtual Humans
@inproceedings{tsai_study_2012,
title = {A Study of Emotional Contagion with Virtual Characters},
author = {Jason Tsai and Emma Bowring and Stacy C. Marsella and Wendy Wood and Milind Tambe},
url = {http://ict.usc.edu/pubs/A%20Study%20of%20Emotional%20Contagion%20with%20Virtual%20Characters.pdf},
year = {2012},
date = {2012-09-01},
booktitle = {The 12th International Conference on Intelligent Virtual Agents (IVA)},
address = {Santa Cruz, CA},
abstract = {In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people's emotions [10]. In this paper, we perform a battery of experiments to explore the existence of agent-human emotional contagion. The first study is a between subjects design, wherein subjects were shown an image of a character's face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase in self-reported happiness between Neutral and Happy conditions with all characters tested. In a second study, we examine the effect of a virtual character's presence in a strategic decision by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic situation within the Stag Hunt and conducts the same experiment using a description of the same strategic situation with the decision already made. We find that the emotional impact returns, implying that the contagion effect is substantially lessened in the presence of a strategic decision.},
keywords = {Social Simulation, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people's emotions [10]. In this paper, we perform a battery of experiments to explore the existence of agent-human emotional contagion. The first study is a between subjects design, wherein subjects were shown an image of a character's face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase in self-reported happiness between Neutral and Happy conditions with all characters tested. In a second study, we examine the effect of a virtual character's presence in a strategic decision by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic situation within the Stag Hunt and conducts the same experiment using a description of the same strategic situation with the decision already made. We find that the emotional impact returns, implying that the contagion effect is substantially lessened in the presence of a strategic decision.
Traum, David; DeVault, David; Lee, Jina; Wang, Zhiyang; Marsella, Stacy C.
Incremental Dialogue Understanding and Feedback for Multi-party, Multimodal Conversation Proceedings Article
In: The 12th International Conference on Intelligent Virtual Agents (IVA), Santa Cruz, CA, 2012.
Abstract | Links | BibTeX | Tags: Social Simulation, Virtual Humans
@inproceedings{traum_incremental_2012,
title = {Incremental Dialogue Understanding and Feedback for Multi-party, Multimodal Conversation},
author = {David Traum and David DeVault and Jina Lee and Zhiyang Wang and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Incremental%20Dialogue%20Understanding%20and%20Feedback%20for%20Multi-party%20Multimodal%20Conversation.pdf},
year = {2012},
date = {2012-09-01},
booktitle = {The 12th International Conference on Intelligent Virtual Agents (IVA)},
address = {Santa Cruz, CA},
abstract = {In order to provide comprehensive listening behavior, virtual humans engaged in dialogue need to incrementally listen, interpret, understand, and react to what someone is saying, in real time, as they are saying it. In this paper, we describe an implemented system for engaging in multiparty dialogue, including incremental understanding and a range of feedback. We present an FML message extension for feedback in multipary dialogue that can be connected to a feedback realizer. We also describe how the important aspects of that message are calculated by different modules involved in partial input processing as a speaker is talking in a multiparty dialogue.},
keywords = {Social Simulation, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
In order to provide comprehensive listening behavior, virtual humans engaged in dialogue need to incrementally listen, interpret, understand, and react to what someone is saying, in real time, as they are saying it. In this paper, we describe an implemented system for engaging in multiparty dialogue, including incremental understanding and a range of feedback. We present an FML message extension for feedback in multipary dialogue that can be connected to a feedback realizer. We also describe how the important aspects of that message are calculated by different modules involved in partial input processing as a speaker is talking in a multiparty dialogue.
Pynadath, David V.; Marsella, Stacy C.
Socio-Cultural Modeling through Decision-Theoretic Agents with Theory of Mind Proceedings Article
In: The 2nd International Conference on Cross-Cultural Decision Making, San Francisco, CA, 2012.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{pynadath_socio-cultural_2012,
title = {Socio-Cultural Modeling through Decision-Theoretic Agents with Theory of Mind},
author = {David V. Pynadath and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Socio-Cultural%20Modeling%20through%20Decision-Theoretic%20Agents%20with%20Theory%20of%20Mind.pdf},
year = {2012},
date = {2012-07-01},
booktitle = {The 2nd International Conference on Cross-Cultural Decision Making},
address = {San Francisco, CA},
abstract = {PsychSim is an agent-based social simulation framework that captures a wide range of the individual and cultural differences exhibited in complex social scenar- ios. PsychSim takes a decision-theoretic approach to modeling Theory of Mind, giving its agents a rich space of beliefs and preferences. PsychSim also uses a unique piecewise linear representation language that allows it to reason backward from observed behavior to infer consistent parameter settings. We first applied PsychSim to the exploratory simulation of influence campaigns in the face of a heterogeneous socio-cultural arena of operations. More recently, we have used PsychSim in a range of simulation-based training systems designed to teach skills such as the correct use of language, gestures, and social norms of a foreign culture, cross-cultural negotiation, avoidance of risky behavior, and urban stabilization operations.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
PsychSim is an agent-based social simulation framework that captures a wide range of the individual and cultural differences exhibited in complex social scenar- ios. PsychSim takes a decision-theoretic approach to modeling Theory of Mind, giving its agents a rich space of beliefs and preferences. PsychSim also uses a unique piecewise linear representation language that allows it to reason backward from observed behavior to infer consistent parameter settings. We first applied PsychSim to the exploratory simulation of influence campaigns in the face of a heterogeneous socio-cultural arena of operations. More recently, we have used PsychSim in a range of simulation-based training systems designed to teach skills such as the correct use of language, gestures, and social norms of a foreign culture, cross-cultural negotiation, avoidance of risky behavior, and urban stabilization operations.
Wang, Ning; Pynadath, David V.; Marsella, Stacy C.
Toward Automatic Verification of Multiagent Systems for Training Simulations Proceedings Article
In: International Conference on Intelligent Tutoring Systems, pp. 151–161, Crete, 2012.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{wang_toward_2012,
title = {Toward Automatic Verification of Multiagent Systems for Training Simulations},
author = {Ning Wang and David V. Pynadath and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Toward%20Automatic%20Veri%EF%AC%81cation%20of%20Multiagent%20Systems%20for%20Training%20Simulations.pdf},
year = {2012},
date = {2012-06-01},
booktitle = {International Conference on Intelligent Tutoring Systems},
pages = {151–161},
address = {Crete},
abstract = {Advances in multiagent systems have led to their successful application in experiential training simulations, where students learn by interacting with agents who represent people, groups, structures, etc. These multiagent simulations must model the training scenario so that the students’ success is correlated with the degree to which they follow the intended pedagogy. As these simulations increase in size and richness, it becomes harder to guarantee that the agents accurately encode the pedagogy. Testing with human subjects provides the most accurate feedback, but it can explore only a limited subspace of simulation paths. In this paper, we present a mechanism for using human data to verify the degree to which the simulation encodes the intended pedagogy. Starting with an analysis of data from a deployed multiagent training simulation, we then present an automated mechanism for using the human data to generate a distribution appropriate for sampling simulation paths. By generalizing from a small set of human data, the automated approach can systematically explore a much larger space of possible training paths and verify the degree to which a multiagent training simulation adheres to its intended pedagogy.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Advances in multiagent systems have led to their successful application in experiential training simulations, where students learn by interacting with agents who represent people, groups, structures, etc. These multiagent simulations must model the training scenario so that the students’ success is correlated with the degree to which they follow the intended pedagogy. As these simulations increase in size and richness, it becomes harder to guarantee that the agents accurately encode the pedagogy. Testing with human subjects provides the most accurate feedback, but it can explore only a limited subspace of simulation paths. In this paper, we present a mechanism for using human data to verify the degree to which the simulation encodes the intended pedagogy. Starting with an analysis of data from a deployed multiagent training simulation, we then present an automated mechanism for using the human data to generate a distribution appropriate for sampling simulation paths. By generalizing from a small set of human data, the automated approach can systematically explore a much larger space of possible training paths and verify the degree to which a multiagent training simulation adheres to its intended pedagogy.
Tsai, Jason; Bowring, Emma; Marsella, Stacy C.; Wood, Wendy; Tambe, Milind
Preliminary Exploration of Agent-Human Emotional Contagion via Static Expressions Proceedings Article
In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Valencia, Spain, 2012.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{tsai_preliminary_2012,
title = {Preliminary Exploration of Agent-Human Emotional Contagion via Static Expressions},
author = {Jason Tsai and Emma Bowring and Stacy C. Marsella and Wendy Wood and Milind Tambe},
url = {http://ict.usc.edu/pubs/Preliminary%20Exploration%20of%20Agent-Human%20Emotional%20Contagion%20via%20Static%20Expressions.pdf},
year = {2012},
date = {2012-06-01},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
address = {Valencia, Spain},
abstract = {In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [13]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents’ expressions must be accurately understood to avoid undesirable repercussions. In this paper, we perform a battery of experiments to explore the existence of agent-human emotional contagion. The first study is a between-subjects design, wherein subjects were shown an image of a character’s face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase in self-reported happiness between Neutral and Happy conditions with all characters tested and, to our knowledge, is the first ever study explicitly showing emotional contagion from a virtual agent to a human. We also examine the effects of participant gender, participant ethnicity, character attractiveness, and perceived character happiness and find that only perceived character happiness has a substantial impact on emotional contagion. In a second study, we examine the effect of a virtual character’s presence in a strategic situation by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic decision within the Stag Hunt and conducts the same experiment using a description of the same strategic situation with the decision already made. We find that the emotional impact returns again, particularly for women, implying that the contagion effect is substantially lessened in the presence of a strategic decision.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [13]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents’ expressions must be accurately understood to avoid undesirable repercussions. In this paper, we perform a battery of experiments to explore the existence of agent-human emotional contagion. The first study is a between-subjects design, wherein subjects were shown an image of a character’s face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase in self-reported happiness between Neutral and Happy conditions with all characters tested and, to our knowledge, is the first ever study explicitly showing emotional contagion from a virtual agent to a human. We also examine the effects of participant gender, participant ethnicity, character attractiveness, and perceived character happiness and find that only perceived character happiness has a substantial impact on emotional contagion. In a second study, we examine the effect of a virtual character’s presence in a strategic situation by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic decision within the Stag Hunt and conducts the same experiment using a description of the same strategic situation with the decision already made. We find that the emotional impact returns again, particularly for women, implying that the contagion effect is substantially lessened in the presence of a strategic decision.
2011
Chen, Junda; Demski, Abram; Han, Teawon; Morency, Louis-Philippe; Pynadath, David V.; Rafidi, Nicole; Rosenbloom, Paul
Fusing symbolic and decision-theoretic problem solving + perception in a graphical cognitive architecture Proceedings Article
In: Proceedings of the 2nd International Conference on Biologically Inspired Cognitive Architecture (BICA), Arlington, VA, 2011.
Abstract | Links | BibTeX | Tags: CogArch, Cognitive Architecture, Social Simulation, Virtual Humans
@inproceedings{chen_fusing_2011,
title = {Fusing symbolic and decision-theoretic problem solving + perception in a graphical cognitive architecture},
author = {Junda Chen and Abram Demski and Teawon Han and Louis-Philippe Morency and David V. Pynadath and Nicole Rafidi and Paul Rosenbloom},
url = {http://ict.usc.edu/pubs/Fusing%20symbolic%20and%20decision-theoretic%20problem%20solving%20+%20perception%20in%20a%20graphical%20cognitive%20architecture.pdf},
year = {2011},
date = {2011-11-01},
booktitle = {Proceedings of the 2nd International Conference on Biologically Inspired Cognitive Architecture (BICA)},
address = {Arlington, VA},
abstract = {A step is taken towards fusing symbolic and decision-theoretic problem solving in a cognitive architecture by implementing the latter in an architecture within which the former has already been demonstrated. The graphical models upon which the architecture is based enable a uniform implementation of both varieties of problem solving. They also enable a uniform combination with forms of decision-relevant perception, highlighting a potential path towards a tight coupling between central cognition and peripheral perception.},
keywords = {CogArch, Cognitive Architecture, Social Simulation, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
A step is taken towards fusing symbolic and decision-theoretic problem solving in a cognitive architecture by implementing the latter in an architecture within which the former has already been demonstrated. The graphical models upon which the architecture is based enable a uniform implementation of both varieties of problem solving. They also enable a uniform combination with forms of decision-relevant perception, highlighting a potential path towards a tight coupling between central cognition and peripheral perception.
Klatt, Jennifer; Marsella, Stacy C.; Krämer, Nicole C.
Negotiations in the Context of AIDS Prevention: An Agent-Based Model Using Theory of Mind Proceedings Article
In: The 11th International Conference on Intelligent Virtual Agents (IVA 2011), Reykjavik, Iceland, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{klatt_negotiations_2011,
title = {Negotiations in the Context of AIDS Prevention: An Agent-Based Model Using Theory of Mind},
author = {Jennifer Klatt and Stacy C. Marsella and Nicole C. Krämer},
url = {http://ict.usc.edu/pubs/Negotiations%20in%20the%20Context%20of%20AIDS%20Prevention-%20An%20Agent-Based%20Model%20Using%20Theory%20of%20Mind.pdf},
year = {2011},
date = {2011-09-01},
booktitle = {The 11th International Conference on Intelligent Virtual Agents (IVA 2011)},
address = {Reykjavik, Iceland},
abstract = {For the purpose of an AIDS prevention game, a model was developed that focuses on training safe sex negotiations. Non-player characters in the game are socially intelligent agents that are equipped with a Theory of Mind that allows them to reason about the mental processes and behavior of others. The underlying model for the negotiation about safe sex between player and agent was implemented in multi-agent simulation software. It consists of two agents who have different goals of either safe or unsafe sex, actions to achieve these goals, and the wish to come to an agreement. The model was evaluated for the agent-agent conversation to test the basic functioning.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
For the purpose of an AIDS prevention game, a model was developed that focuses on training safe sex negotiations. Non-player characters in the game are socially intelligent agents that are equipped with a Theory of Mind that allows them to reason about the mental processes and behavior of others. The underlying model for the negotiation about safe sex between player and agent was implemented in multi-agent simulation software. It consists of two agents who have different goals of either safe or unsafe sex, actions to achieve these goals, and the wish to come to an agreement. The model was evaluated for the agent-agent conversation to test the basic functioning.
Lee, Jina; Marsella, Stacy C.
Modeling Side Participants and Bystanders: The Importance of Being a Laugh Track Proceedings Article
In: Proceedings of the 11th Conference on Intelligent Virtual Agents, Reykjavik, Iceland, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{lee_modeling_2011,
title = {Modeling Side Participants and Bystanders: The Importance of Being a Laugh Track},
author = {Jina Lee and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Modeling%20Side%20Participants%20and%20Bystanders.pdf},
year = {2011},
date = {2011-09-01},
booktitle = {Proceedings of the 11th Conference on Intelligent Virtual Agents},
address = {Reykjavik, Iceland},
abstract = {Research in virtual agents has largely ignored the role and behavior of side participants and especially bystanders. Our view is that the behavior of these other participants is critical in multi-party interactions, especially in interactive drama. In this paper, we provide an analysis of nonverbal behaviors associated with these roles. We ⬚first review studies of interpersonal relationships and nonverbal behavior. From this review, we construct an analysis framework based on characters' interpersonal relationships, conversational roles, and communicative acts. We then assess this framework by analyzing improv sessions of an old west scenario involving 4 characters. Informed by this analysis, we implemented a general model for participant and bystander behavior.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Research in virtual agents has largely ignored the role and behavior of side participants and especially bystanders. Our view is that the behavior of these other participants is critical in multi-party interactions, especially in interactive drama. In this paper, we provide an analysis of nonverbal behaviors associated with these roles. We ⬚first review studies of interpersonal relationships and nonverbal behavior. From this review, we construct an analysis framework based on characters' interpersonal relationships, conversational roles, and communicative acts. We then assess this framework by analyzing improv sessions of an old west scenario involving 4 characters. Informed by this analysis, we implemented a general model for participant and bystander behavior.
Chiu, Chung-Cheng; Marsella, Stacy C.
How to train your avatar: A data driven approach to gesture generation Proceedings Article
In: The 11th International Conference on Intelligent Virtual Agents (IVA 2011), ReykjavÃk, Iceland, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{chiu_how_2011,
title = {How to train your avatar: A data driven approach to gesture generation},
author = {Chung-Cheng Chiu and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/How%20to%20train%20your%20avatar.pdf},
year = {2011},
date = {2011-09-01},
booktitle = {The 11th International Conference on Intelligent Virtual Agents (IVA 2011)},
address = {ReykjavÃk, Iceland},
abstract = {The ability to gesture is key to realizing virtual characters that can engage in face-to-face interaction with people. Many applications take an approach of predefining possible utterances of a virtual character and building all the gesture animations needed for those utterances. We can save effort on building a virtual human if we can construct a general gesture controller that will generate behavior for novel utterances. Because the dynamics of human gestures are related to the prosody of speech, in this work we propose a model to generate gestures based on prosody. We then assess the naturalness of the animations by comparing them against human gestures. The evaluation results were promising, human judgments show no significant difference between our generated gestures and human gestures and the generated gestures were judged as significantly better than real human gestures from a different utterance.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
The ability to gesture is key to realizing virtual characters that can engage in face-to-face interaction with people. Many applications take an approach of predefining possible utterances of a virtual character and building all the gesture animations needed for those utterances. We can save effort on building a virtual human if we can construct a general gesture controller that will generate behavior for novel utterances. Because the dynamics of human gestures are related to the prosody of speech, in this work we propose a model to generate gestures based on prosody. We then assess the naturalness of the animations by comparing them against human gestures. The evaluation results were promising, human judgments show no significant difference between our generated gestures and human gestures and the generated gestures were judged as significantly better than real human gestures from a different utterance.
Wang, Zhiyang; Lee, Jina; Marsella, Stacy C.
Towards More Comprehensive Listening Behavior: Beyond the Bobble Head Proceedings Article
In: The 11th International Conference on Intelligent Virtual Agents (IVA 2011), ReykjavÃk, Iceland, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{wang_towards_2011,
title = {Towards More Comprehensive Listening Behavior: Beyond the Bobble Head},
author = {Zhiyang Wang and Jina Lee and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Towards%20More%20Comprehensive%20Listening%20Behavior-%20Beyond%20the%20Bobble%20Head.pdf},
year = {2011},
date = {2011-09-01},
booktitle = {The 11th International Conference on Intelligent Virtual Agents (IVA 2011)},
address = {ReykjavÃk, Iceland},
abstract = {Realizing effective listening behavior in virtual humans has become a key area of research, especially as research has sought to realize more complex social scenarios involving multiple participants and bystanders. A human listener's nonverbal behavior is conditioned by a variety of factors, from current speaker's behavior to the listener's role and desire to participate in the conversation and unfolding comprehension of the speaker. Similarly, we seek to create virtual humans able to provide feedback based on their participatory goals and their partial understanding of, and reaction to, the relevance of what the speaker is saying as the speaker speaks. Based on a survey of existing psychological literature as well as recent technological advances in recognition and partial understanding of natural language, we describe a model of how to integrate these factors into a virtual human that behaves consistently with these goals.We then discuss how the model is implemented into a virtual human architecture and present an evaluation of behaviors used in the model.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Realizing effective listening behavior in virtual humans has become a key area of research, especially as research has sought to realize more complex social scenarios involving multiple participants and bystanders. A human listener's nonverbal behavior is conditioned by a variety of factors, from current speaker's behavior to the listener's role and desire to participate in the conversation and unfolding comprehension of the speaker. Similarly, we seek to create virtual humans able to provide feedback based on their participatory goals and their partial understanding of, and reaction to, the relevance of what the speaker is saying as the speaker speaks. Based on a survey of existing psychological literature as well as recent technological advances in recognition and partial understanding of natural language, we describe a model of how to integrate these factors into a virtual human that behaves consistently with these goals.We then discuss how the model is implemented into a virtual human architecture and present an evaluation of behaviors used in the model.
Ito, Jonathan Y.; Marsella, Stacy C.
Contextually-Based Utility: An Appraisal-Based Approach at Modeling Framing and Decisions Proceedings Article
In: The 25th Conference on Artificail Intelligence (AAAI-11), San Francisco, CA, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{ito_contextually-based_2011,
title = {Contextually-Based Utility: An Appraisal-Based Approach at Modeling Framing and Decisions},
author = {Jonathan Y. Ito and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Contextually-Based%20Utility-%20An%20Appraisal-Based%20Approach%20at%20Modeling%20Framing%20and%20Decisions.pdf},
year = {2011},
date = {2011-08-01},
booktitle = {The 25th Conference on Artificail Intelligence (AAAI-11)},
address = {San Francisco, CA},
abstract = {Creating accurate computational models of human decision making is a vital step towards the realization of socially intelligent systems capable of both predicting and simulating human behavior. In modeling human decision making, a key factor is the psychological phenomenon known as "framing", in which the preferences of a decision maker change in response to contextual changes in decision problems. Existing approaches treat framing as a one-dimensional contextual influence based on the perception of outcomes as either gains or losses. However, empirical studies have shown that framing effects are much more multifaceted than one-dimensional views of framing suggest. To address this limitation, we propose an integrative approach to modeling framing which combines the psychological principles of cognitive appraisal theories and decision-theoretic notions of utility and probability. We show that this approach allows for both the identification and computation of the salient contextual factors in a decision as well as modeling how they ultimately affect the decision process. Furthermore, we show that our multidimensional, appraisal-based approach can account for framing effects identified in the empirical literature which cannot be addressed by one-dimensional theories, thereby promising more accurate models of human behavior.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Creating accurate computational models of human decision making is a vital step towards the realization of socially intelligent systems capable of both predicting and simulating human behavior. In modeling human decision making, a key factor is the psychological phenomenon known as "framing", in which the preferences of a decision maker change in response to contextual changes in decision problems. Existing approaches treat framing as a one-dimensional contextual influence based on the perception of outcomes as either gains or losses. However, empirical studies have shown that framing effects are much more multifaceted than one-dimensional views of framing suggest. To address this limitation, we propose an integrative approach to modeling framing which combines the psychological principles of cognitive appraisal theories and decision-theoretic notions of utility and probability. We show that this approach allows for both the identification and computation of the salient contextual factors in a decision as well as modeling how they ultimately affect the decision process. Furthermore, we show that our multidimensional, appraisal-based approach can account for framing effects identified in the empirical literature which cannot be addressed by one-dimensional theories, thereby promising more accurate models of human behavior.
Melo, Celso M.; Gratch, Jonathan; Carnevale, Peter
Reverse Appraisal: Inferring from Emotion Displays who is the Cooperator and the Competitor in a Social Dilemma Proceedings Article
In: The 33rd Annual Meeting of the Cognitive Science Society (CogSci) 2011, Boston, MA, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation, Virtual Humans
@inproceedings{de_melo_reverse_2011,
title = {Reverse Appraisal: Inferring from Emotion Displays who is the Cooperator and the Competitor in a Social Dilemma},
author = {Celso M. Melo and Jonathan Gratch and Peter Carnevale},
url = {http://ict.usc.edu/pubs/Reverse%20Appraisal-%20Inferring%20from%20Emotion%20Displays%20who%20is%20the%20Cooperator%20and%20the%20Competitor%20in%20a%20Social%20Dilemma.pdf},
year = {2011},
date = {2011-07-01},
booktitle = {The 33rd Annual Meeting of the Cognitive Science Society (CogSci) 2011},
address = {Boston, MA},
abstract = {This paper explores whether and how facial displays of emotion can impact emergence of cooperation in a social dilemma. Three experiments are described where participants play the iterated prisoner's dilemma with (computer) players that display emotion. Experiment 1 compares a cooperative player, whose displays reflect a goal of mutual cooperation, with a control player that shows no emotion. Experiment 2 compares a competitive player, whose displays reflect a goal of getting more points than the participant, and the control player. Experiment 3 compares the cooperative and competitive players. Results show that people: cooperate more with the cooperative than the control player (Experiment 1); do not cooperate differently with the competitive and control players (Experiment 2); and, cooperate more with the cooperative than the competitive player, when they play the latter first (Experiment 3). In line with a social functions view of emotion, we argue people infer, from emotion displays, the other player's propensity to cooperate by reversing the emotion appraisal process. Post- game surveys show that people interpret the emotion displays according to appraisal variables (desirability, responsibility and controllability) in ways that are consistent with predictions from appraisal theories of emotion.},
keywords = {Social Simulation, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper explores whether and how facial displays of emotion can impact emergence of cooperation in a social dilemma. Three experiments are described where participants play the iterated prisoner's dilemma with (computer) players that display emotion. Experiment 1 compares a cooperative player, whose displays reflect a goal of mutual cooperation, with a control player that shows no emotion. Experiment 2 compares a competitive player, whose displays reflect a goal of getting more points than the participant, and the control player. Experiment 3 compares the cooperative and competitive players. Results show that people: cooperate more with the cooperative than the control player (Experiment 1); do not cooperate differently with the competitive and control players (Experiment 2); and, cooperate more with the cooperative than the competitive player, when they play the latter first (Experiment 3). In line with a social functions view of emotion, we argue people infer, from emotion displays, the other player's propensity to cooperate by reversing the emotion appraisal process. Post- game surveys show that people interpret the emotion displays according to appraisal variables (desirability, responsibility and controllability) in ways that are consistent with predictions from appraisal theories of emotion.
Tsai, Jason; Fridman, Natalie; Bowring, Emma; Brown, Matthew; Epstein, Shira; Kaminka, Gal; Marsella, Stacy C.; Ogden, Andrew; Rika, Inbal; Sheel, Ankur; Taylor, Matthew; Wang, Xuezhi; Zilka, Avishay; Tambe, Milind
ESCAPES - Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social comparison Proceedings Article
In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Taipei, Taiwan, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{tsai_escapes_2011,
title = {ESCAPES - Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social comparison},
author = {Jason Tsai and Natalie Fridman and Emma Bowring and Matthew Brown and Shira Epstein and Gal Kaminka and Stacy C. Marsella and Andrew Ogden and Inbal Rika and Ankur Sheel and Matthew Taylor and Xuezhi Wang and Avishay Zilka and Milind Tambe},
url = {http://ict.usc.edu/pubs/ESCAPES%20-%20Evacuation%20Simulation%20with%20Children,%20Authorities,%20Parents,%20Emotions,%20and%20Social%20comparison.pdf},
year = {2011},
date = {2011-05-01},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
address = {Taipei, Taiwan},
abstract = {In creating an evacuation simulation for training and planning, realistic agents that reproduce known phenomenon are required. Evacuation simulation in the airport domain requires additional features beyond most simulations, including the unique behaviors of first time visitors who have incomplete knowledge of the area and families that do not necessarily adhere to often-assumed pedestrian behaviors. Evacuation simulations not customized for the airport domain do not incorporate the factors important to it, leading to inaccuracies when applied to it. In this paper, we describe ESCAPES, a multiagent evacuation simulation tool that incorporates four key features: (i) different agent types; (ii) emotional interactions; (iii) informational interactions; (iv) behavioral interactions. Our simulator reproduces phenomena observed in existing studies on evacuation scenarios and the features we incorporate substantially impact escape time. We use ESCAPES to model the International Terminal at Los Angeles International Airport (LAX) and receive high praise from security officials.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
In creating an evacuation simulation for training and planning, realistic agents that reproduce known phenomenon are required. Evacuation simulation in the airport domain requires additional features beyond most simulations, including the unique behaviors of first time visitors who have incomplete knowledge of the area and families that do not necessarily adhere to often-assumed pedestrian behaviors. Evacuation simulations not customized for the airport domain do not incorporate the factors important to it, leading to inaccuracies when applied to it. In this paper, we describe ESCAPES, a multiagent evacuation simulation tool that incorporates four key features: (i) different agent types; (ii) emotional interactions; (iii) informational interactions; (iv) behavioral interactions. Our simulator reproduces phenomena observed in existing studies on evacuation scenarios and the features we incorporate substantially impact escape time. We use ESCAPES to model the International Terminal at Los Angeles International Airport (LAX) and receive high praise from security officials.
Chiu, Chung-Cheng; Marsella, Stacy C.
A style controller for generating virtual human behaviors Proceedings Article
In: The Tenth International Conference on Autonomous Agents and Multiagent Systems, Taipei, Taiwan, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{chiu_style_2011,
title = {A style controller for generating virtual human behaviors},
author = {Chung-Cheng Chiu and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/A%20style%20controller%20for%20generating%20virtual%20human%20behaviors.pdf},
year = {2011},
date = {2011-05-01},
booktitle = {The Tenth International Conference on Autonomous Agents and Multiagent Systems},
address = {Taipei, Taiwan},
abstract = {Creating a virtual character that exhibits realistic physical behaviors requires a rich set of animations. To mimic the variety as well as the subtlety of human behavior, we may need to animate not only a wide range of behaviors but also variations of the same type of behavior influenced by the environment and the state of the character, including the emotional and physiological state. A general approach to this challenge is to gather a set of animations produced by artists or motion capture. However, this approach can be extremely costly in time and effort. In this work, we propose a model that can learn styled motion generation and an algorithm that produce new styles of motions via style interpolation. The model takes a set of styled motions as training samples and creates new motions that are the generalization among the given styles. Our style interpolation algorithm can blend together motions with distinct styles, and improves on the performance of previous work. We verify our algorithm using walking motions of different styles, and the experimental results show that our method is signiï¬cantly better than previous work.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Creating a virtual character that exhibits realistic physical behaviors requires a rich set of animations. To mimic the variety as well as the subtlety of human behavior, we may need to animate not only a wide range of behaviors but also variations of the same type of behavior influenced by the environment and the state of the character, including the emotional and physiological state. A general approach to this challenge is to gather a set of animations produced by artists or motion capture. However, this approach can be extremely costly in time and effort. In this work, we propose a model that can learn styled motion generation and an algorithm that produce new styles of motions via style interpolation. The model takes a set of styled motions as training samples and creates new motions that are the generalization among the given styles. Our style interpolation algorithm can blend together motions with distinct styles, and improves on the performance of previous work. We verify our algorithm using walking motions of different styles, and the experimental results show that our method is signiï¬cantly better than previous work.
Pynadath, David V.; Si, Mei; Marsella, Stacy C.
Modeling Theory of Mind and Cognitive Appraisal with Decision-Theoretic Agents Book Section
In: Appraisal, pp. 1–30, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@incollection{pynadath_modeling_2011,
title = {Modeling Theory of Mind and Cognitive Appraisal with Decision-Theoretic Agents},
author = {David V. Pynadath and Mei Si and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Modeling%20Theory%20of%20Mind%20and%20Cognitive%20Appraisal%20with%20Decision-Theoretic%20Agents.pdf},
year = {2011},
date = {2011-04-01},
booktitle = {Appraisal},
pages = {1–30},
abstract = {Agent-based simulation of human social behavior has become increasingly important as a basic research tool to further our understanding of social behavior, as well as to create virtual social worlds used to both entertain and educate. A key factor in human social interaction is our beliefs about others as intentional agents, a Theory of Mind. How we act depends not only on the immediate effect of our actions but also on how we believe others will react. In this paper, we discuss PsychSim, an implemented multiagent-based simulation tool for modeling social interaction and influence. While typical approaches to such modeling have used first-order logic, PsychSim agents have their own decision-theoretic models of the world, including beliefs about their environment and recursive models of other agents. Using these quantitative models of uncertainty and preferences, we have translated existing psychological theories into a decision-theoretic semantics that allow the agents to reason about degrees of believability in a novel way. We demonstrate the expressiveness of PsychSim’s decision-theoretic implementation of Theory of Mind by presenting its use as the foundation for a domain-independent model of appraisal theory, the leading psychological theory of emotion. The model of appraisal within PsychSim demonstrates the key role of a Theory of Mind capacity in appraisal and social emotions, as well as arguing for a uniform process for emotion and cognition.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {incollection}
}
Agent-based simulation of human social behavior has become increasingly important as a basic research tool to further our understanding of social behavior, as well as to create virtual social worlds used to both entertain and educate. A key factor in human social interaction is our beliefs about others as intentional agents, a Theory of Mind. How we act depends not only on the immediate effect of our actions but also on how we believe others will react. In this paper, we discuss PsychSim, an implemented multiagent-based simulation tool for modeling social interaction and influence. While typical approaches to such modeling have used first-order logic, PsychSim agents have their own decision-theoretic models of the world, including beliefs about their environment and recursive models of other agents. Using these quantitative models of uncertainty and preferences, we have translated existing psychological theories into a decision-theoretic semantics that allow the agents to reason about degrees of believability in a novel way. We demonstrate the expressiveness of PsychSim’s decision-theoretic implementation of Theory of Mind by presenting its use as the foundation for a domain-independent model of appraisal theory, the leading psychological theory of emotion. The model of appraisal within PsychSim demonstrates the key role of a Theory of Mind capacity in appraisal and social emotions, as well as arguing for a uniform process for emotion and cognition.
Miller, Lynn C.; Marsella, Stacy C.; Dey, Teresa; Appleby, Paul Robert; Christensen, John L.; Klatt, Jennifer; Read, Stephen J.
Socially Optimized Learning in Virtual Environments (SOLVE) Proceedings Article
In: 4th International Conference on Interactive Digital Storytelling, Vancouver, British Columbia, 2011.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{miller_socially_2011,
title = {Socially Optimized Learning in Virtual Environments (SOLVE)},
author = {Lynn C. Miller and Stacy C. Marsella and Teresa Dey and Paul Robert Appleby and John L. Christensen and Jennifer Klatt and Stephen J. Read},
url = {http://ict.usc.edu/pubs/Socially%20Optimized%20Learning%20in%20Virtual%20Environments%20(SOL%20VE).pdf},
year = {2011},
date = {2011-01-01},
booktitle = {4th International Conference on Interactive Digital Storytelling},
address = {Vancouver, British Columbia},
abstract = {Although young men who have sex with men (MSM) are at high risk for contracting HIV, few interventions address the affective/automatic factors (e.g., sexual arousal, shame/stigma) that may precipitate young MSM’s risk- taking. A National Institutes of Health (NIH)-funded DVD interactive video intervention that simulated a "virtual date" with guides/mentors reduced sexual risk over 3-months for Black, Latino and Caucasian young MSM. In the current work, limitations of the DVD format (e.g., number of different risk challenges MSM encounter; DVD quickly becomes dated) were addressed with 3-D animated intelligent agents/interactive digital storytelling using a Unity Game platform. The development (e.g., design, art, social science formative research, etc.) of this NIH funded game for changing risky behavior is described as well as the ongoing national randomized "on-line" evaluation over 6 months.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Although young men who have sex with men (MSM) are at high risk for contracting HIV, few interventions address the affective/automatic factors (e.g., sexual arousal, shame/stigma) that may precipitate young MSM’s risk- taking. A National Institutes of Health (NIH)-funded DVD interactive video intervention that simulated a "virtual date" with guides/mentors reduced sexual risk over 3-months for Black, Latino and Caucasian young MSM. In the current work, limitations of the DVD format (e.g., number of different risk challenges MSM encounter; DVD quickly becomes dated) were addressed with 3-D animated intelligent agents/interactive digital storytelling using a Unity Game platform. The development (e.g., design, art, social science formative research, etc.) of this NIH funded game for changing risky behavior is described as well as the ongoing national randomized "on-line" evaluation over 6 months.
2010
Si, Mei; Marsella, Stacy C.; Pynadath, David V.
Importance of Well-Motivated Characters in Interactive Narratives: An Empirical Evaluation Proceedings Article
In: 2010 International Conference on Interactive Digital Storytelling, Edinburgh, UK, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{si_importance_2010,
title = {Importance of Well-Motivated Characters in Interactive Narratives: An Empirical Evaluation},
author = {Mei Si and Stacy C. Marsella and David V. Pynadath},
url = {http://ict.usc.edu/pubs/Importance%20of%20Well-Motivated%20Characters%20in%20Interactive%20Narratives.pdf},
year = {2010},
date = {2010-11-01},
booktitle = {2010 International Conference on Interactive Digital Storytelling},
address = {Edinburgh, UK},
abstract = {http://ict.usc.edu/pubs/Importance%20of%20Well-Motivated%20Characters%20in%20Interactive%20Narratives.pdf},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
http://ict.usc.edu/pubs/Importance%20of%20Well-Motivated%20Characters%20in%20Interactive%20Narratives.pdf
Lance, Brent; Marsella, Stacy C.
The Expressive Gaze Model: Using Gaze to Express Emotion Journal Article
In: Computer Graphics and Applications, IEEE, vol. 30, no. 4, pp. 62–73, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@article{lance_expressive_2010,
title = {The Expressive Gaze Model: Using Gaze to Express Emotion},
author = {Brent Lance and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/The_Expressive_Gaze_Model.pdf},
year = {2010},
date = {2010-08-01},
journal = {Computer Graphics and Applications, IEEE},
volume = {30},
number = {4},
pages = {62–73},
abstract = {The Expressive Gaze Model is a hierarchical framework for composing simple behaviors into emotionally expressive gaze shifts for virtual characters. Its primary components are the Gaze Warping Transformation, which generates emotionally expressive head and torso movement in a gaze shift, and an eye movement model.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {article}
}
The Expressive Gaze Model is a hierarchical framework for composing simple behaviors into emotionally expressive gaze shifts for virtual characters. Its primary components are the Gaze Warping Transformation, which generates emotionally expressive head and torso movement in a gaze shift, and an eye movement model.
Ito, Jonathan Y.; Pynadath, David V.; Sonenberg, Liz; Marsella, Stacy C.
Wishful Thinking in Effective Decision Making (Extended Abstract) Proceedings Article
In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto, Ontario, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{ito_wishful_2010,
title = {Wishful Thinking in Effective Decision Making (Extended Abstract)},
author = {Jonathan Y. Ito and David V. Pynadath and Liz Sonenberg and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Wishful_thinking_extended_abstract.pdf},
year = {2010},
date = {2010-05-01},
booktitle = {Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems},
volume = {1},
address = {Toronto, Ontario},
abstract = {Creating agents that act reasonably in uncertain environments is a primary goal of agent-based research. In this work we explore the theory that wishful thinking can be an effective strategy in uncertain and competitive decision scenarios. Specifically, we present the constraints necessary for wishful thinking to outperform Expected Utility Maximization and take instances of popular games from Game-Theoretic literature showing how they relate to our constraints and whether they can benefit from wishful-thinking.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Creating agents that act reasonably in uncertain environments is a primary goal of agent-based research. In this work we explore the theory that wishful thinking can be an effective strategy in uncertain and competitive decision scenarios. Specifically, we present the constraints necessary for wishful thinking to outperform Expected Utility Maximization and take instances of popular games from Game-Theoretic literature showing how they relate to our constraints and whether they can benefit from wishful-thinking.
Ito, Jonathan Y.; Pynadath, David V.; Marsella, Stacy C.
Modeling self-deception within a decision-theoretic framework Journal Article
In: Autonomous Agent Multi-Agent Systems, vol. 20, pp. 3–13, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@article{ito_modeling_2010,
title = {Modeling self-deception within a decision-theoretic framework},
author = {Jonathan Y. Ito and David V. Pynadath and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Modeling%20self-deception%20within%20a%20decision-theoretic%20framework.pdf},
doi = {10.1007/s10458-009-9096-7},
year = {2010},
date = {2010-05-01},
journal = {Autonomous Agent Multi-Agent Systems},
volume = {20},
pages = {3–13},
abstract = {Computational modeling of human belief maintenance and decision-making processes has become increasingly important for a wide range of applications. In this paper, we present a framework for modeling the human capacity for self-deception from a decision-theoretic perspective in which we describe an integrated process of wishful thinking which includes the determination of a desired belief state, the biasing of internal beliefs towards or away from this desired belief state, and the final decision-making process. Finally, we show that in certain situations self-deception can be beneficial.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {article}
}
Computational modeling of human belief maintenance and decision-making processes has become increasingly important for a wide range of applications. In this paper, we present a framework for modeling the human capacity for self-deception from a decision-theoretic perspective in which we describe an integrated process of wishful thinking which includes the determination of a desired belief state, the biasing of internal beliefs towards or away from this desired belief state, and the final decision-making process. Finally, we show that in certain situations self-deception can be beneficial.
Si, Mei; Marsella, Stacy C.; Pynadath, David V.
Evaluating Directorial Control in a Character-Centric Interactive Narrative Framework Proceedings Article
In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Toronto, Canada, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{si_evaluating_2010,
title = {Evaluating Directorial Control in a Character-Centric Interactive Narrative Framework},
author = {Mei Si and Stacy C. Marsella and David V. Pynadath},
url = {http://ict.usc.edu/pubs/Evaluating%20Directorial%20Control%20in%20a%20Character-Centric%20Interactive%20Narrative%20Framework.pdf},
year = {2010},
date = {2010-05-01},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
address = {Toronto, Canada},
abstract = {Interactive narrative allows the user to play a role in a story and interact with other characters controlled by the system. Directorial control is a procedure for dynamically tuning the interaction towards the author's desired effects. Most existing approaches for directorial control are built within plot-centric frameworks for interactive narrative and do not have a systematic way to ensure that the characters are always well-motivated during the interaction. Thespian is a character-centric framework for interactive narrative. In our previous work on Thespian, we presented an approach for applying directorial control while not affecting the consistency of characters' motivations. This work evaluates the effectiveness of our directorial control approach. Given the priority of generating only well-motivated characters' behaviors, we empirically evaluate how often the author's desired effects are achieved. We also discuss how the directorial control procedure can save the author effort in configuring the characters.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Interactive narrative allows the user to play a role in a story and interact with other characters controlled by the system. Directorial control is a procedure for dynamically tuning the interaction towards the author's desired effects. Most existing approaches for directorial control are built within plot-centric frameworks for interactive narrative and do not have a systematic way to ensure that the characters are always well-motivated during the interaction. Thespian is a character-centric framework for interactive narrative. In our previous work on Thespian, we presented an approach for applying directorial control while not affecting the consistency of characters' motivations. This work evaluates the effectiveness of our directorial control approach. Given the priority of generating only well-motivated characters' behaviors, we empirically evaluate how often the author's desired effects are achieved. We also discuss how the directorial control procedure can save the author effort in configuring the characters.
Si, Mei; Marsella, Stacy C.
Modeling Rich Characters in Interactive Narrative Games Proceedings Article
In: GAMEON-ASIA 2010, Shanghai, China, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{si_modeling_2010-1,
title = {Modeling Rich Characters in Interactive Narrative Games},
author = {Mei Si and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Modeling%20Rich%20Characters%20in%20Interactive%20Narrative%20Games.pdf},
year = {2010},
date = {2010-03-01},
booktitle = {GAMEON-ASIA 2010},
address = {Shanghai, China},
abstract = {Computing technologies have advanced rapidly over the past decade. Faster machines, better graphics, and more advanced algorithms become available every year. Moreover, the evolution of internet technology and the increasing accessibility of computing resources and mobile devices allow computing technologies to go beyond business and scientific computing, and become an important means for providing entertainment and facilitating communication. These advances have helped to enable a new form of media – interactive narrative games. Interactive narrative games allow a user to play a role in a story and interact with other characters driven by AI agents. The user's choices affect the unfolding of the story. Because of the support of user interactivity and the use of computer simulated virtual environments, interactive narrative games are closely related to video games. In fact, the rapid growth of interest in interactive narrative games is in part motivated by the explosion of computer-based games in recent years. Compared to more traditional forms of video games, such as arcade games, action games, and even role playing games, interactive narrative games emphasize more of the social and narrative aspects of the experi- ence. Story, of course, is a central part of the human experience both as entertainment and as a powerful tool for providing pedagogy. We watch movies, read novels and tell stories. Interactive narrative games provide an experience that integrates user agency with the engaging power of narrative. Interactive narrative games have been recognized as a promising tool for providing both pedagogy and entertainment. They have been proposed for a range of training applications, e.g. [13, 20, 26, 35, 22] as well as entertainment applications, e.g. [10, 23, 3, 12, 11, 36]. In this paper, we discuss the design desiderata for interactive narrative games, and in particular for creating the virtual characters in interactive narrative games. We argue that a rich model of characters that are well-motivated, socially aware and have a "Theory of Mind" is needed. We discuss the state of the art work on modeling virtual characters. In particular, we present the approaches taken in Thes- pian [27, 26, 28, 29, 31, 30] – a decision-theoretic multi-agent framework for interactive narratives.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Computing technologies have advanced rapidly over the past decade. Faster machines, better graphics, and more advanced algorithms become available every year. Moreover, the evolution of internet technology and the increasing accessibility of computing resources and mobile devices allow computing technologies to go beyond business and scientific computing, and become an important means for providing entertainment and facilitating communication. These advances have helped to enable a new form of media – interactive narrative games. Interactive narrative games allow a user to play a role in a story and interact with other characters driven by AI agents. The user's choices affect the unfolding of the story. Because of the support of user interactivity and the use of computer simulated virtual environments, interactive narrative games are closely related to video games. In fact, the rapid growth of interest in interactive narrative games is in part motivated by the explosion of computer-based games in recent years. Compared to more traditional forms of video games, such as arcade games, action games, and even role playing games, interactive narrative games emphasize more of the social and narrative aspects of the experi- ence. Story, of course, is a central part of the human experience both as entertainment and as a powerful tool for providing pedagogy. We watch movies, read novels and tell stories. Interactive narrative games provide an experience that integrates user agency with the engaging power of narrative. Interactive narrative games have been recognized as a promising tool for providing both pedagogy and entertainment. They have been proposed for a range of training applications, e.g. [13, 20, 26, 35, 22] as well as entertainment applications, e.g. [10, 23, 3, 12, 11, 36]. In this paper, we discuss the design desiderata for interactive narrative games, and in particular for creating the virtual characters in interactive narrative games. We argue that a rich model of characters that are well-motivated, socially aware and have a "Theory of Mind" is needed. We discuss the state of the art work on modeling virtual characters. In particular, we present the approaches taken in Thes- pian [27, 26, 28, 29, 31, 30] – a decision-theoretic multi-agent framework for interactive narratives.
Si, Mei; Marsella, Stacy C.; Pynadath, David V.
Modeling appraisal in theory of mind reasoning Proceedings Article
In: Journal of Autonomous Agents and Multi-Agent Systems; Proceedings of the 8th International Conference on Intelligent Virtual Agents, pp. 14–31, Tokyo, Japan, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{si_modeling_2010,
title = {Modeling appraisal in theory of mind reasoning},
author = {Mei Si and Stacy C. Marsella and David V. Pynadath},
url = {http://ict.usc.edu/pubs/Modeling%20appraisal%20in%20theory%20of%20mind%20reasoning.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {Journal of Autonomous Agents and Multi-Agent Systems; Proceedings of the 8th International Conference on Intelligent Virtual Agents},
volume = {20(1)},
pages = {14–31},
address = {Tokyo, Japan},
abstract = {Cognitive appraisal theories, which link human emotional experience to their interpretations of events happening in the environment, are leading approaches to model emotions. Cognitive appraisal theories have often been used both for simulating "real emotions" in virtual characters and for predicting the human user's emotional experience to facilitate human-computer interaction. In this work, we investigate the computational modeling of appraisal in a multi-agent decision-theoretic framework using Partially Observable Markov Decision Process-based (POMDP) agents. Domain-independent approaches are developed for five key appraisal dimensions (motivational relevance, motivation congruence, accountability, control and novelty). We also discuss how the modeling of theory of mind (recursive beliefs about self and others) is realized in the agents and is critical for simulating social emotions. Our model of appraisal is applied to three different scenarios to illustrate its usages. This work not only provides a solution for computationally modeling emotion in POMDPbased agents, but also illustrates the tight relationship between emotion and cognition – the appraisal dimensions are derived from the processes and information required for the agent's decision-making and beliefmaintenance processes, which suggests a uniform cognitive structure for emotion and cognition.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Cognitive appraisal theories, which link human emotional experience to their interpretations of events happening in the environment, are leading approaches to model emotions. Cognitive appraisal theories have often been used both for simulating "real emotions" in virtual characters and for predicting the human user's emotional experience to facilitate human-computer interaction. In this work, we investigate the computational modeling of appraisal in a multi-agent decision-theoretic framework using Partially Observable Markov Decision Process-based (POMDP) agents. Domain-independent approaches are developed for five key appraisal dimensions (motivational relevance, motivation congruence, accountability, control and novelty). We also discuss how the modeling of theory of mind (recursive beliefs about self and others) is realized in the agents and is critical for simulating social emotions. Our model of appraisal is applied to three different scenarios to illustrate its usages. This work not only provides a solution for computationally modeling emotion in POMDPbased agents, but also illustrates the tight relationship between emotion and cognition – the appraisal dimensions are derived from the processes and information required for the agent's decision-making and beliefmaintenance processes, which suggests a uniform cognitive structure for emotion and cognition.
Lee, Jina; Wang, Zhiyang; Marsella, Stacy C.
Evaluating Models of Speaker Head Nods for Virtual Agents Proceedings Article
In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Toronto, Canada, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{lee_evaluating_2010,
title = {Evaluating Models of Speaker Head Nods for Virtual Agents},
author = {Jina Lee and Zhiyang Wang and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Evaluating%20Models%20of%20Speaker%20Head%20Nods%20for%20Virtual%20Agents.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
address = {Toronto, Canada},
abstract = {Virtual human research has often modeled nonverbal behaviors based on the findings of psychological research. In recent years, however, there have been growing efforts to use automated, data-driven approaches to find patterns of nonverbal behaviors in video corpora and even thereby discover new factors that have not been previously documented. However, there have been few studies that compare how the behaviors generated by different approaches are interpreted by people. In this paper, we present an evaluation study to compare the perception of nonverbal behaviors, more specifically head nods, generated by different approaches. Studies have shown that head nods serve a variety of communicative functions and that the head is in constant motion during speaking turns. To evaluate the different approaches of head nod generation, we asked human subjects to evaluate videos of a virtual agent displaying nods generated by a human, by a machine learning data-driven approach, or by a handcrafted rule-based approach. Results show that there is a significant effect on the perception of head nods in terms of appropriate nod occurrence, especially between the data driven approach and the rule-based approach.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual human research has often modeled nonverbal behaviors based on the findings of psychological research. In recent years, however, there have been growing efforts to use automated, data-driven approaches to find patterns of nonverbal behaviors in video corpora and even thereby discover new factors that have not been previously documented. However, there have been few studies that compare how the behaviors generated by different approaches are interpreted by people. In this paper, we present an evaluation study to compare the perception of nonverbal behaviors, more specifically head nods, generated by different approaches. Studies have shown that head nods serve a variety of communicative functions and that the head is in constant motion during speaking turns. To evaluate the different approaches of head nod generation, we asked human subjects to evaluate videos of a virtual agent displaying nods generated by a human, by a machine learning data-driven approach, or by a handcrafted rule-based approach. Results show that there is a significant effect on the perception of head nods in terms of appropriate nod occurrence, especially between the data driven approach and the rule-based approach.
Putten, Astrid M.; Kramer, Nicole C.; Gratch, Jonathan
How Our Personality Shapes Our Interactions with Virtual Characters - Implications for Research and Development Proceedings Article
In: 10th International Conference on Intelligent Virtual Agents, Philadelphia, PA, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation, Virtual Humans
@inproceedings{von_der_putten_how_2010,
title = {How Our Personality Shapes Our Interactions with Virtual Characters - Implications for Research and Development},
author = {Astrid M. Putten and Nicole C. Kramer and Jonathan Gratch},
url = {http://ict.usc.edu/pubs/How%20Our%20Personality%20Shapes%20Our%20Interactions%20with%20Virtual%20Characters%20-%20Implications%20for%20Research%20and%20Development.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {10th International Conference on Intelligent Virtual Agents},
address = {Philadelphia, PA},
abstract = {here is a general lack of awareness for the influence of users' personality traits on human-agent-interaction (HAI). Numerous studies do not even consider explanatory variables like age and gender although they are easily accessible. The present study focuses on explaining the occurrence of social effects in HAI. Apart from the original manipulation of the study we assessed the users' traits. Results show that participants' personality traits those traits which relate to persistent behavioral patterns in social contact (agreeableness, extraversion, approach avoidance, self-efficacy in monitoring others, shyness, public self-consciousness) were found to be predictive, whereas other personality traits and gender and age did not affect the evaluation. Results suggest that personality traits are better predictors for the evaluation outcome than the actual behavior of the agent as it has been manipulated in the experiment. Implications for the research on and development of virtual agents are discussed.},
keywords = {Social Simulation, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
here is a general lack of awareness for the influence of users' personality traits on human-agent-interaction (HAI). Numerous studies do not even consider explanatory variables like age and gender although they are easily accessible. The present study focuses on explaining the occurrence of social effects in HAI. Apart from the original manipulation of the study we assessed the users' traits. Results show that participants' personality traits those traits which relate to persistent behavioral patterns in social contact (agreeableness, extraversion, approach avoidance, self-efficacy in monitoring others, shyness, public self-consciousness) were found to be predictive, whereas other personality traits and gender and age did not affect the evaluation. Results suggest that personality traits are better predictors for the evaluation outcome than the actual behavior of the agent as it has been manipulated in the experiment. Implications for the research on and development of virtual agents are discussed.
Lance, Brent; Marsella, Stacy C.
Glances, Glares, and Glowering: How Should a Virtual Human Express Emotion Through Gaze? Journal Article
In: Journal Autonomous Agents and Multi-Agent Systems, vol. 20, no. 1, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation
@article{lance_glances_2010,
title = {Glances, Glares, and Glowering: How Should a Virtual Human Express Emotion Through Gaze?},
author = {Brent Lance and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Glances,%20Glares,%20and%20Glowering-%20How%20Should%20a%20Virtual%20Human%20Express%20Emotion%20Through%20Gaze.pdf},
year = {2010},
date = {2010-01-01},
journal = {Journal Autonomous Agents and Multi-Agent Systems},
volume = {20},
number = {1},
abstract = {Gaze is an extremely powerful expressive signal that is used for many purposes, from expressing emotion to regulating human interaction. The use of gaze as a signal has been exploited to strong effect in hand-animated characters, greatly enhancing the believability of the character's simulated life. However, virtual humans animated in real-time have been less successful at using expressive gaze. One reason for this is that a gaze shift towards any speci?c target can be performed in many different ways, using many different expressive manners of gaze, each of which can potentially imply a different emotional or cognitive internal state. However, there is currently no mapping that describes how a user will attribute these internal states to a virtual character performing a gaze shift in a particular manner. In this paper, we begin to address this by providing the results of an empirical study that explores the mapping between an observer's attribution of emotional state to gaze. The purpose of this mapping is to allow for an interactive virtual human to generate believable gaze shifts that a user will attribute a desired emotional state to. We have generated a set of animations by composing low-level gaze attributes culled from the nonverbal behavior literature. Then, subjects judged the animations displaying these attributes. While the results do not provide a complete mapping between gaze and emotion, they do provide a basis for a generative model of expressive gaze.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {article}
}
Gaze is an extremely powerful expressive signal that is used for many purposes, from expressing emotion to regulating human interaction. The use of gaze as a signal has been exploited to strong effect in hand-animated characters, greatly enhancing the believability of the character's simulated life. However, virtual humans animated in real-time have been less successful at using expressive gaze. One reason for this is that a gaze shift towards any speci?c target can be performed in many different ways, using many different expressive manners of gaze, each of which can potentially imply a different emotional or cognitive internal state. However, there is currently no mapping that describes how a user will attribute these internal states to a virtual character performing a gaze shift in a particular manner. In this paper, we begin to address this by providing the results of an empirical study that explores the mapping between an observer's attribution of emotional state to gaze. The purpose of this mapping is to allow for an interactive virtual human to generate believable gaze shifts that a user will attribute a desired emotional state to. We have generated a set of animations by composing low-level gaze attributes culled from the nonverbal behavior literature. Then, subjects judged the animations displaying these attributes. While the results do not provide a complete mapping between gaze and emotion, they do provide a basis for a generative model of expressive gaze.
Marsella, Stacy C.; Gratch, Jonathan; Petta, Paola
Computational Models of Emotion Book Section
In: Scherer, K. R.; Bänziger, T.; Roesch, (Ed.): A blueprint for an affectively competent agent: Cross-fertilization between Emotion Psychology, Affective Neuroscience, and Affective Computing, Oxford University Press, Oxford, 2010.
Abstract | Links | BibTeX | Tags: Social Simulation, Virtual Humans
@incollection{marsella_computational_2010,
title = {Computational Models of Emotion},
author = {Stacy C. Marsella and Jonathan Gratch and Paola Petta},
editor = {K. R. Scherer and T. Bänziger and Roesch},
url = {http://ict.usc.edu/pubs/Computational%20Models%20of%20Emotion.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {A blueprint for an affectively competent agent: Cross-fertilization between Emotion Psychology, Affective Neuroscience, and Affective Computing},
publisher = {Oxford University Press},
address = {Oxford},
abstract = {Recent years have seen a significant expansion in research on computational models of human emotional processes, driven both by their potential for basic research on emotion and cognition as well as their promise for an ever increasing range of applications. This has led to a truly interdisciplinary, mutually beneficial partnership between emotion research in psychology and computational science, of which this volume is an exemplar. To understand this partnership and its potential for transforming existing practices in emotion research across disciplines and for disclosing important novel areas of research, we explore in this chapter the history of work in computational models of emotion including the various uses to which they have been put, the theoretical traditions that have shaped their development, and how these uses and traditions are reflected in their underlying architectures.},
keywords = {Social Simulation, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
Recent years have seen a significant expansion in research on computational models of human emotional processes, driven both by their potential for basic research on emotion and cognition as well as their promise for an ever increasing range of applications. This has led to a truly interdisciplinary, mutually beneficial partnership between emotion research in psychology and computational science, of which this volume is an exemplar. To understand this partnership and its potential for transforming existing practices in emotion research across disciplines and for disclosing important novel areas of research, we explore in this chapter the history of work in computational models of emotion including the various uses to which they have been put, the theoretical traditions that have shaped their development, and how these uses and traditions are reflected in their underlying architectures.
2009
Si, Mei; Marsella, Stacy C.; Pynadath, David V.
Directorial Control in a Decision-Theoretic Framework for Interactive Narrative Proceedings Article
In: Proceedings of the International Conference on Interactive Digital Storytelling, Guimarães, Portugal, 2009.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{si_directorial_2009,
title = {Directorial Control in a Decision-Theoretic Framework for Interactive Narrative},
author = {Mei Si and Stacy C. Marsella and David V. Pynadath},
url = {http://ict.usc.edu/pubs/Directorial%20Control%20in%20a%20Decision-Theoretic%20Framework%20for%20Interactive%20Narrative.pdf},
year = {2009},
date = {2009-12-01},
booktitle = {Proceedings of the International Conference on Interactive Digital Storytelling},
address = {Guimarães, Portugal},
abstract = {Computer aided interactive narrative has received increasing attention in recent years. Automated directorial control that manages the development of the story in the face of user interaction is an important aspect of interactive narrative design. Most existing approaches lack an explicit model of the user. This limits the approaches' ability of predicting the user's experience, and hence undermines the effectiveness of the approaches. Thespian is a multi-agent framework for authoring and simulating interactive narratives with explicit models of the user. This work extends Thespian with the ability to provide proactive directorial control using the user model. In this paper, we present the algorithms in detail, followed by examples.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Computer aided interactive narrative has received increasing attention in recent years. Automated directorial control that manages the development of the story in the face of user interaction is an important aspect of interactive narrative design. Most existing approaches lack an explicit model of the user. This limits the approaches' ability of predicting the user's experience, and hence undermines the effectiveness of the approaches. Thespian is a multi-agent framework for authoring and simulating interactive narratives with explicit models of the user. This work extends Thespian with the ability to provide proactive directorial control using the user model. In this paper, we present the algorithms in detail, followed by examples.
Lee, Jina; Marsella, Stacy C.; Prendinger, Helmut; Neviarouskaya, Alena
Learning a Model of Speaker Head Nods using Gesture Corpora Proceedings Article
In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Budapest, Hungary, 2009.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{lee_learning_2009-1,
title = {Learning a Model of Speaker Head Nods using Gesture Corpora},
author = {Jina Lee and Stacy C. Marsella and Helmut Prendinger and Alena Neviarouskaya},
url = {http://ict.usc.edu/pubs/learning%20a%20model%20of%20speaker%20head%20nods.pdf},
year = {2009},
date = {2009-10-01},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
address = {Budapest, Hungary},
abstract = {During face-to-face conversation, the speaker's head is continually in motion. These movements serve a variety of important communicative functions, and may also be influ- enced by our emotions. The goal for this work is to build a domain-independent model of speaker's head movements and investigate the effect of using affective information dur- ing the learning process. Once the model is learned, it can later be used to generate head movements for virtual agents. In this paper, we describe our machine-learning approach to predict speaker's head nods using an annotated corpora of face-to-face human interaction and emotion labels gener- ated by an affect recognition model. We describe the feature selection process, training process, and the comparison of results of the learned models under varying conditions. The results show that using affective information can help pre- dict head nods better than when no affective information is used.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
During face-to-face conversation, the speaker's head is continually in motion. These movements serve a variety of important communicative functions, and may also be influ- enced by our emotions. The goal for this work is to build a domain-independent model of speaker's head movements and investigate the effect of using affective information dur- ing the learning process. Once the model is learned, it can later be used to generate head movements for virtual agents. In this paper, we describe our machine-learning approach to predict speaker's head nods using an annotated corpora of face-to-face human interaction and emotion labels gener- ated by an affect recognition model. We describe the feature selection process, training process, and the comparison of results of the learned models under varying conditions. The results show that using affective information can help pre- dict head nods better than when no affective information is used.
Lee, Jina; Marsella, Stacy C.
Learning Models of Speaker Head Nods with Affective Information Proceedings Article
In: The 3rd International Conference on Affective Computing and Intelligent Interaction (ACII 2009), Amsterdam, The Netherlands, 2009.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{lee_learning_2009,
title = {Learning Models of Speaker Head Nods with Affective Information},
author = {Jina Lee and Stacy C. Marsella},
url = {http://ict.usc.edu/pubs/Learning%20Models%20of%20Speaker%20Head%20Nods%20with%20Affective%20Information.pdf},
year = {2009},
date = {2009-09-01},
booktitle = {The 3rd International Conference on Affective Computing and Intelligent Interaction (ACII 2009)},
address = {Amsterdam, The Netherlands},
abstract = {During face-to-face conversation, the speaker's head is continually in motion. These movements serve a variety of important communicative functions, and may also be influ- enced by our emotions. The goal for this work is to build a domain-independent model of speaker's head movements and investigate the effect of using affective information dur- ing the learning process. Once the model is learned, it can later be used to generate head movements for virtual agents. In this paper, we describe our machine-learning approach to predict speaker's head nods using an annotated corpora of face-to-face human interaction and emotion labels gener- ated by an affect recognition model. We describe the feature selection process, training process, and the comparison of results of the learned models under varying conditions. The results show that using affective information can help pre- dict head nods better than when no affective information is used.},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
During face-to-face conversation, the speaker's head is continually in motion. These movements serve a variety of important communicative functions, and may also be influ- enced by our emotions. The goal for this work is to build a domain-independent model of speaker's head movements and investigate the effect of using affective information dur- ing the learning process. Once the model is learned, it can later be used to generate head movements for virtual agents. In this paper, we describe our machine-learning approach to predict speaker's head nods using an annotated corpora of face-to-face human interaction and emotion labels gener- ated by an affect recognition model. We describe the feature selection process, training process, and the comparison of results of the learned models under varying conditions. The results show that using affective information can help pre- dict head nods better than when no affective information is used.
Marsella, Stacy C.; Gratch, Jonathan; Wang, Ning
Assessing the validity of a computational model of emotional coping Proceedings Article
In: International Conference on Affective Computing and Intelligent Interaction, Amsterdam, The Netherlands, 2009.
Abstract | Links | BibTeX | Tags: Social Simulation, Virtual Humans
@inproceedings{marsella_assessing_2009,
title = {Assessing the validity of a computational model of emotional coping},
author = {Stacy C. Marsella and Jonathan Gratch and Ning Wang},
url = {http://ict.usc.edu/pubs/Assessing%20the%20validity%20of%20a%20computational%20model%20of%20emotional%20coping.pdf},
year = {2009},
date = {2009-09-01},
booktitle = {International Conference on Affective Computing and Intelligent Interaction},
address = {Amsterdam, The Netherlands},
abstract = {In this paper we describe the results of a rigorous empirical study evaluating the coping responses of a computational model of emotion. We discuss three key kinds of coping, Wishful Thinking, Resignation and Dis-tancing that impact an agent's beliefs, intentions and desires, and compare these coping responses to related work in the attitude change literature. We discuss the EMA computational model of emotion and identify sev-eral hypotheses it makes concerning these coping processes. We assess these hypotheses against the beha-vior of human subjects playing a competitive board game, using monetary gains and losses to induce emo-tion and coping. Subject's appraisals, emotional state and coping responses were indexed at key points throughout a game, revealing a pattern of subject's al-tering their beliefs, desires and intentions as the game unfolds. The results clearly support several of the hypo-theses on coping responses but also identify (a) exten-sions to how EMA models Wishful Thinking as well as (b) individual differences in subject's coping responses.},
keywords = {Social Simulation, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we describe the results of a rigorous empirical study evaluating the coping responses of a computational model of emotion. We discuss three key kinds of coping, Wishful Thinking, Resignation and Dis-tancing that impact an agent's beliefs, intentions and desires, and compare these coping responses to related work in the attitude change literature. We discuss the EMA computational model of emotion and identify sev-eral hypotheses it makes concerning these coping processes. We assess these hypotheses against the beha-vior of human subjects playing a competitive board game, using monetary gains and losses to induce emo-tion and coping. Subject's appraisals, emotional state and coping responses were indexed at key points throughout a game, revealing a pattern of subject's al-tering their beliefs, desires and intentions as the game unfolds. The results clearly support several of the hypo-theses on coping responses but also identify (a) exten-sions to how EMA models Wishful Thinking as well as (b) individual differences in subject's coping responses.
Marsella, Stacy C.; Lee, Jina
Predicting Speaker Head Nods and the Effects of Affective Information Proceedings Article
In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009.
Abstract | Links | BibTeX | Tags: Social Simulation
@inproceedings{marsella_predicting_2009,
title = {Predicting Speaker Head Nods and the Effects of Affective Information},
author = {Stacy C. Marsella and Jina Lee},
url = {http://ict.usc.edu/pubs/Predicting%20Speaker%20Head%20Nods%20and%20the%20Effects%20of%20Affective%20Information.pdf},
year = {2009},
date = {2009-09-01},
booktitle = {3rd International Conference on Affective Computing and Intelligent Interaction and Workshops},
volume = {12},
abstract = {During face-to-face conversation, our body is continually in motion with various head, gesture, and posture movements. Based on findings of the communicative functions served by these nonverbal behaviors, many virtual agent systems have modeled them to make the virtual agent look more effective and believable. One channel of nonverbal behaviors that has received less attention is head movements, despite the important functions served by them. The goal for this work is to build a domain-independent model of speaker's head movements that could be used to generate head movements for virtual agents. In this paper, we present a machine learning approach for learning models of head movements by focusing on when speaker head nods should occur and conduct evaluation studies that compare the nods generated by this work to our previous approach of using hand-crafted rules},
keywords = {Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
During face-to-face conversation, our body is continually in motion with various head, gesture, and posture movements. Based on findings of the communicative functions served by these nonverbal behaviors, many virtual agent systems have modeled them to make the virtual agent look more effective and believable. One channel of nonverbal behaviors that has received less attention is head movements, despite the important functions served by them. The goal for this work is to build a domain-independent model of speaker's head movements that could be used to generate head movements for virtual agents. In this paper, we present a machine learning approach for learning models of head movements by focusing on when speaker head nods should occur and conduct evaluation studies that compare the nods generated by this work to our previous approach of using hand-crafted rules
McAlinden, Ryan; Gordon, Andrew S.; Lane, H. Chad; Pynadath, David V.
UrbanSim: A Game-based Simulation for Counterinsurgency and Stability-focused Operations Proceedings Article
In: Workshop on Intelligent Educational Games, 14th International Conference on Artificial Intelligence in Education, Brighton, UK, 2009.
Abstract | Links | BibTeX | Tags: Learning Sciences, Social Simulation, The Narrative Group
@inproceedings{mcalinden_urbansim_2009,
title = {UrbanSim: A Game-based Simulation for Counterinsurgency and Stability-focused Operations},
author = {Ryan McAlinden and Andrew S. Gordon and H. Chad Lane and David V. Pynadath},
url = {http://ict.usc.edu/pubs/UrbanSim-%20A%20Game-based%20Simulation%20for%20Counterinsurgency%20and%20Stability-focused%20Operations.pdf},
year = {2009},
date = {2009-07-01},
booktitle = {Workshop on Intelligent Educational Games, 14th International Conference on Artificial Intelligence in Education},
address = {Brighton, UK},
abstract = {The UrbanSim Learning Package is a simulation-based training application designed for the U.S. Army to develop commanders' skills for conducting counterinsurgency operations. UrbanSim incorporates multiple artificial intelligence (AI) technologies in order to provide an effective training experience, three of which are described in this paper. First, UrbanSim simulates the mental attitudes and actions of groups and individuals in an urban environment using the PsychSim reasoning engine. Second, UrbanSim interjects narrative elements into the training experience using a case-based story engine, driven by non-fiction stories told by experienced commanders. Third, UrbanSim provides intelligent tutoring using a simulation-based method for eliciting and evaluating learner decisions. UrbanSim represents a confluence of AI techniques that seek to bridge the gap between basic research and deployed AI systems.},
keywords = {Learning Sciences, Social Simulation, The Narrative Group},
pubstate = {published},
tppubtype = {inproceedings}
}
The UrbanSim Learning Package is a simulation-based training application designed for the U.S. Army to develop commanders' skills for conducting counterinsurgency operations. UrbanSim incorporates multiple artificial intelligence (AI) technologies in order to provide an effective training experience, three of which are described in this paper. First, UrbanSim simulates the mental attitudes and actions of groups and individuals in an urban environment using the PsychSim reasoning engine. Second, UrbanSim interjects narrative elements into the training experience using a case-based story engine, driven by non-fiction stories told by experienced commanders. Third, UrbanSim provides intelligent tutoring using a simulation-based method for eliciting and evaluating learner decisions. UrbanSim represents a confluence of AI techniques that seek to bridge the gap between basic research and deployed AI systems.