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881.
Gratch, Jonathan
Socially Situated Planning Incollection
In: Socially Intelligent Agents, Multiagent Systems, Artificial Societies, and Simulated Organizations, vol. 3, pp. 181–188, AAAI Fall Symposium on Socially Intelligent Agents - The Human in the Loop, North Falmouth, MA, 2000.
@incollection{gratch_socially_2000,
title = {Socially Situated Planning},
author = {Jonathan Gratch},
url = {http://ict.usc.edu/pubs/Socially%20Situated%20Planning.pdf},
year = {2000},
date = {2000-01-01},
booktitle = {Socially Intelligent Agents, Multiagent Systems, Artificial Societies, and Simulated Organizations},
volume = {3},
pages = {181--188},
address = {AAAI Fall Symposium on Socially Intelligent Agents - The Human in the Loop, North Falmouth, MA},
abstract = {Introduction: Virtual environments such as training simulators and video games do an impressive job at modeling the physical dynamics of synthetic worlds but fall short when modeling the social dynamics of anything but the most impoverished human encounters. Yet the social dimension is at least as important as good graphics for creating an engaging game or effective training tool. Commercial flight simulators accurately model the technical aspects of flight but many aviation disasters arise from social breakdowns: poor management skills in the cockpit, or the effects of stress and emotion. Perhaps the biggest consumer of simulation technology, the U.S. military, identifies unrealistic human and organizational behavior as a major limitation of existing simulation technology (NRC, 1998). And of course the entertainment industry has long recognized the importance of good character, emotional attachment and rich social interactions to "put butts in seats." This article describes a research effort to endow virtual training environments with richer models of social behavior. We have been developing autonomous and semi-autonomous software agents that plan and act while situated in a social network of other entities, human and synthetic (Hill et. al, 1997; Tambe, 1997; Gratch and Hill, 1999). My work has focused on making agents act in an organization and obey social constraints, coordinate their behavior, negotiate conflicts, but also obey their own self-interest and show a range of individual differences in their behavior and willingness to violate social norms, albeit within the relatively narrow context of a specific training exercise.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Introduction: Virtual environments such as training simulators and video games do an impressive job at modeling the physical dynamics of synthetic worlds but fall short when modeling the social dynamics of anything but the most impoverished human encounters. Yet the social dimension is at least as important as good graphics for creating an engaging game or effective training tool. Commercial flight simulators accurately model the technical aspects of flight but many aviation disasters arise from social breakdowns: poor management skills in the cockpit, or the effects of stress and emotion. Perhaps the biggest consumer of simulation technology, the U.S. military, identifies unrealistic human and organizational behavior as a major limitation of existing simulation technology (NRC, 1998). And of course the entertainment industry has long recognized the importance of good character, emotional attachment and rich social interactions to "put butts in seats." This article describes a research effort to endow virtual training environments with richer models of social behavior. We have been developing autonomous and semi-autonomous software agents that plan and act while situated in a social network of other entities, human and synthetic (Hill et. al, 1997; Tambe, 1997; Gratch and Hill, 1999). My work has focused on making agents act in an organization and obey social constraints, coordinate their behavior, negotiate conflicts, but also obey their own self-interest and show a range of individual differences in their behavior and willingness to violate social norms, albeit within the relatively narrow context of a specific training exercise.
882.
Gratch, Jonathan
Èmile: Marshalling Passions in Training and Education Inproceedings
In: Proceedings of the 4th International Conference on Autonomous Agents, pp. 325–332, Barcelona, Spain, 2000.
@inproceedings{gratch_emile_2000,
title = {Èmile: Marshalling Passions in Training and Education},
author = {Jonathan Gratch},
url = {http://ict.usc.edu/pubs/Emile-%20Marshalling%20Passions%20in%20Training%20and%20Education.pdf},
year = {2000},
date = {2000-01-01},
booktitle = {Proceedings of the 4th International Conference on Autonomous Agents},
pages = {325--332},
address = {Barcelona, Spain},
abstract = {Emotional reasoning can be an important contribution to automated tutoring and training systems. This paper describes �mile, a model of emotional reasoning that builds upon existing approaches and significantly generalizes and extends their capabilities. The main contribution is to show how an explicit planning model allows a more general treatment of several stages of the reasoning process. The model supports educational applications by allowing agents to appraise the emotional significance of events as they relate to students' (or their own) plans and goals, model and predict the emotional state of others, and alter behavior accordingly.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Emotional reasoning can be an important contribution to automated tutoring and training systems. This paper describes �mile, a model of emotional reasoning that builds upon existing approaches and significantly generalizes and extends their capabilities. The main contribution is to show how an explicit planning model allows a more general treatment of several stages of the reasoning process. The model supports educational applications by allowing agents to appraise the emotional significance of events as they relate to students' (or their own) plans and goals, model and predict the emotional state of others, and alter behavior accordingly.
883.
Gratch, Jonathan; Marsella, Stacy C.; Hill, Randall W.; III, LTC George Stone
Deriving Priority Intelligence Requirements for Synthetic Command Entities Inproceedings
In: Proceedings of the 8th Conference on Computer Generated Forces and Behavioral Representation, Orlando, FL, 1999.
@inproceedings{gratch_deriving_1999,
title = {Deriving Priority Intelligence Requirements for Synthetic Command Entities},
author = {Jonathan Gratch and Stacy C. Marsella and Randall W. Hill and LTC George Stone III},
url = {http://ict.usc.edu/pubs/Deriving%20Priority%20Intelligence%20Requirements%20for%20Synthetic%20Command%20Entities.pdf},
year = {1999},
date = {1999-05-01},
booktitle = {Proceedings of the 8th Conference on Computer Generated Forces and Behavioral Representation},
address = {Orlando, FL},
abstract = {Simulation-based training is using increasingly complex synthetic forces. As more complex multiechelon synthetic forces are employed in simulations, the need for a realistic model of their command and control behavior becomes more urgent. In this paper we discuss one key component of such a model, the autonomous generation and use of priority intelligence requirements within multi-echelon plans.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Simulation-based training is using increasingly complex synthetic forces. As more complex multiechelon synthetic forces are employed in simulations, the need for a realistic model of their command and control behavior becomes more urgent. In this paper we discuss one key component of such a model, the autonomous generation and use of priority intelligence requirements within multi-echelon plans.
884.
Gratch, Jonathan; Hill, Randall W.
Continuous Planning and Collaboration for Command and Control in Joint Synthetic Battlespaces Inproceedings
In: Proceedings of the 8th Conference on Computer Generated Forces and Behavioral Representation, Orlando, FL, 1999.
@inproceedings{gratch_continuous_1999,
title = {Continuous Planning and Collaboration for Command and Control in Joint Synthetic Battlespaces},
author = {Jonathan Gratch and Randall W. Hill},
url = {http://ict.usc.edu/pubs/Continuous%20Planning%20and%20Collaboration%20for%20Command%20and%20Control%20in%20Joint%20Synthetic%20Battlespaces.pdf},
year = {1999},
date = {1999-05-01},
booktitle = {Proceedings of the 8th Conference on Computer Generated Forces and Behavioral Representation},
address = {Orlando, FL},
abstract = {In this paper we describe our efforts to model command and control entities for Joint Synthetic Battlespaces. Command agents require a broader repertoire of capabilities than is typically modeled in simulation. They must develop mission plans involving multiple subordinate units, monitor execution, dynamically modify mission plans in response to situational contingencies, collaborate with other decision makers, and deal with a host of organizational issues. We describe our approach to command agent modeling that addresses a number of these issues through its continuous and collaborative approach to mission planning.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we describe our efforts to model command and control entities for Joint Synthetic Battlespaces. Command agents require a broader repertoire of capabilities than is typically modeled in simulation. They must develop mission plans involving multiple subordinate units, monitor execution, dynamically modify mission plans in response to situational contingencies, collaborate with other decision makers, and deal with a host of organizational issues. We describe our approach to command agent modeling that addresses a number of these issues through its continuous and collaborative approach to mission planning.
885.
Traum, David; Andersen, Carl F.; Chong, Waiyian; Josyula, Darsana; Okamoto, Yoshi; Purang, Khemdut; O'Donovan-Anderson, Michael; Perlis, Don
Representations of Dialogue State for Domain and Task Independent Meta-Dialogue Journal Article
In: Electronic Transactions on Artificial Intelligence, vol. 3, pp. 125–152, 1999.
@article{traum_representations_1999,
title = {Representations of Dialogue State for Domain and Task Independent Meta-Dialogue},
author = {David Traum and Carl F. Andersen and Waiyian Chong and Darsana Josyula and Yoshi Okamoto and Khemdut Purang and Michael O'Donovan-Anderson and Don Perlis},
url = {http://ict.usc.edu/pubs/Representations%20of%20Dialogue%20State%20for%20Domain%20and%20Task%20Independent%20Meta-Dialogue.pdf},
year = {1999},
date = {1999-01-01},
journal = {Electronic Transactions on Artificial Intelligence},
volume = {3},
pages = {125--152},
abstract = {We propose a representation of local dialogue context motivated by the need to react appropriately to meta-dialogue, such as various sorts of corrections to the sequence of an instruction and response action. Such contexts includes at least the following aspects: the words and linguistic structures uttered, the domain correlates of those linguistic structures, and plans and actions in response. Each of these is needed as part of the context in order to be able to correctly interpret the range of possible corrections. Partitioning knowledge of dialogue structure in this way may lead to an ability to represent generic dialogue structure (e.g., in the form of axioms), which can be particularized to the domain, topic and content of the dialogue.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We propose a representation of local dialogue context motivated by the need to react appropriately to meta-dialogue, such as various sorts of corrections to the sequence of an instruction and response action. Such contexts includes at least the following aspects: the words and linguistic structures uttered, the domain correlates of those linguistic structures, and plans and actions in response. Each of these is needed as part of the context in order to be able to correctly interpret the range of possible corrections. Partitioning knowledge of dialogue structure in this way may lead to an ability to represent generic dialogue structure (e.g., in the form of axioms), which can be particularized to the domain, topic and content of the dialogue.
886.
Gratch, Jonathan
Why You Should Buy an Emotional Planner Inproceedings
In: Proceedings of the Agents '99 Workshop on Emotion-Based Agent Architectures, 1999.
@inproceedings{gratch_why_1999,
title = {Why You Should Buy an Emotional Planner},
author = {Jonathan Gratch},
url = {http://ict.usc.edu/pubs/Why%20You%20Should%20Buy%20an%20Emotional%20Planner.pdf},
year = {1999},
date = {1999-01-01},
booktitle = {Proceedings of the Agents '99 Workshop on Emotion-Based Agent Architectures},
abstract = {Computation models of emotion have begun to address the problem of how agents arrive at a given emotional state, and how that state might alter their reactions to the environment. Existing work has focused on reactive models of behavior and does not, as of yet, provide much insight on how emotion might relate to the construction and execution of complex plans. This article focuses on this later question. I present a model of how agents ap- praise the emotion significance of events that illustrates a complementary relationship between classical planning methods and models of emotion processing. By building on classical planning methods, the model clarifies prior accounts of emotional appraisal and extends these ac- counts to handle the generation and execution of com- plex multi-agent plans.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Computation models of emotion have begun to address the problem of how agents arrive at a given emotional state, and how that state might alter their reactions to the environment. Existing work has focused on reactive models of behavior and does not, as of yet, provide much insight on how emotion might relate to the construction and execution of complex plans. This article focuses on this later question. I present a model of how agents ap- praise the emotion significance of events that illustrates a complementary relationship between classical planning methods and models of emotion processing. By building on classical planning methods, the model clarifies prior accounts of emotional appraisal and extends these ac- counts to handle the generation and execution of com- plex multi-agent plans.
887.
Gratch, Jonathan
Emotion recognition ≠ Emotion Understanding: Challenges Confronting the Field of Affective Computing Journal Article
In: pp. 9, 0000.
@article{gratch_emotion_nodate,
title = {Emotion recognition ≠ Emotion Understanding: Challenges Confronting the Field of Affective Computing},
author = {Jonathan Gratch},
pages = {9},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
888.
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Journal Article
In: pp. 13, 0000.
@article{gervits_classication-based_nodate,
title = {A Classification-Based Approach to Automating Human-Robot Dialogue},
author = {Felix Gervits and Anton Leuski and Claire Bonial and Carla Gordon and David Traum},
url = {https://link.springer.com/chapter/10.1007/978-981-15-9323-9_10},
doi = {https://doi.org/10.1007/978-981-15-9323-9_10},
pages = {13},
abstract = {We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
889.
Hartholt, Arno; Mozgai, Sharon
From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality Journal Article
In: pp. 35, 0000.
@article{hartholt_combat_nodate,
title = {From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality},
author = {Arno Hartholt and Sharon Mozgai},
pages = {35},
abstract = {Research has documented the efficacy of clinical applications that leverage Virtual Reality (VR) for assessment and treatment purposes across a wide range of domains, including pain, phobias, and posttraumatic stress disorder (PTSD). As the field of Clinical VR matures, it is important to review its origins and examine how these initial explorations have progressed, what gaps remain, and what opportunities the community can pursue. We do this by reflecting on our personal scientific journey against the backdrop of the field in general. In particular, this paper discusses how a clinical research program that was initially designed to deliver trauma-focused VR exposure therapy (VRET) for combat-related PTSD has been evolved to expand its impact and address a wider range of trauma sources. Such trauma sources include sexual trauma and the needs of first responders and healthcare professionals serving on the frontlines of the COVID-19 pandemic. We provide an overview of the field and its general trends, discuss the genesis of our research agenda and its current status, and summarize upcoming opportunities, together with common challenges and lessons learned.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Research has documented the efficacy of clinical applications that leverage Virtual Reality (VR) for assessment and treatment purposes across a wide range of domains, including pain, phobias, and posttraumatic stress disorder (PTSD). As the field of Clinical VR matures, it is important to review its origins and examine how these initial explorations have progressed, what gaps remain, and what opportunities the community can pursue. We do this by reflecting on our personal scientific journey against the backdrop of the field in general. In particular, this paper discusses how a clinical research program that was initially designed to deliver trauma-focused VR exposure therapy (VRET) for combat-related PTSD has been evolved to expand its impact and address a wider range of trauma sources. Such trauma sources include sexual trauma and the needs of first responders and healthcare professionals serving on the frontlines of the COVID-19 pandemic. We provide an overview of the field and its general trends, discuss the genesis of our research agenda and its current status, and summarize upcoming opportunities, together with common challenges and lessons learned.
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