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41.
Kappas, Arvid; Gratch, Jonathan
These Aren’t The Droids You Are Looking for: Promises and Challenges for the Intersection of Affective Science and Robotics/AI Journal Article
In: Affec Sci, 2023, ISSN: 2662-2041, 2662-205X.
@article{kappas_these_2023,
title = {These Aren’t The Droids You Are Looking for: Promises and Challenges for the Intersection of Affective Science and Robotics/AI},
author = {Arvid Kappas and Jonathan Gratch},
url = {https://link.springer.com/10.1007/s42761-023-00211-3},
doi = {10.1007/s42761-023-00211-3},
issn = {2662-2041, 2662-205X},
year = {2023},
date = {2023-08-01},
urldate = {2023-09-20},
journal = {Affec Sci},
abstract = {Abstract
AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do. However, within affective science the focus of research is often to test a particular assumption, such as “smiles affect liking.” Such focus does not provide the information necessary to synthesize affective behavior in long dynamic and real-time interactions. In consequence, theories do not play a large role in the development of artificial affective systems by engineers, but self-learning systems develop their behavior out of large corpora of recorded interactions. The status quo is characterized by measurement issues, theoretical lacunae regarding prevalence and functions of affective behavior in interaction, and underpowered studies that cannot provide the solid empirical foundation for further theoretical developments. This contribution will highlight some of these challenges and point towards next steps to create a rapprochement between engineers and affective scientists with a view to improving theory and solid applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do. However, within affective science the focus of research is often to test a particular assumption, such as “smiles affect liking.” Such focus does not provide the information necessary to synthesize affective behavior in long dynamic and real-time interactions. In consequence, theories do not play a large role in the development of artificial affective systems by engineers, but self-learning systems develop their behavior out of large corpora of recorded interactions. The status quo is characterized by measurement issues, theoretical lacunae regarding prevalence and functions of affective behavior in interaction, and underpowered studies that cannot provide the solid empirical foundation for further theoretical developments. This contribution will highlight some of these challenges and point towards next steps to create a rapprochement between engineers and affective scientists with a view to improving theory and solid applications.
AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do. However, within affective science the focus of research is often to test a particular assumption, such as “smiles affect liking.” Such focus does not provide the information necessary to synthesize affective behavior in long dynamic and real-time interactions. In consequence, theories do not play a large role in the development of artificial affective systems by engineers, but self-learning systems develop their behavior out of large corpora of recorded interactions. The status quo is characterized by measurement issues, theoretical lacunae regarding prevalence and functions of affective behavior in interaction, and underpowered studies that cannot provide the solid empirical foundation for further theoretical developments. This contribution will highlight some of these challenges and point towards next steps to create a rapprochement between engineers and affective scientists with a view to improving theory and solid applications.
42.
Tran, Minh; Yin, Yufeng; Soleymani, Mohammad
Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition Proceedings Article
In: INTERSPEECH 2023, pp. 636–640, ISCA, 2023.
@inproceedings{tran_personalized_2023,
title = {Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition},
author = {Minh Tran and Yufeng Yin and Mohammad Soleymani},
url = {https://www.isca-speech.org/archive/interspeech_2023/tran23c_interspeech.html},
doi = {10.21437/Interspeech.2023-2170},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-23},
booktitle = {INTERSPEECH 2023},
pages = {636–640},
publisher = {ISCA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
43.
Hale, James; Kim, Peter; Gratch, Jonathan
Risk Aversion and Demographic Factors Affect Preference Elicitation and Outcomes of a Salary Negotiation Journal Article
In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. Volume 45, 2023.
@article{hale_risk_2023,
title = {Risk Aversion and Demographic Factors Affect Preference Elicitation and Outcomes of a Salary Negotiation},
author = {James Hale and Peter Kim and Jonathan Gratch},
url = {https://escholarship.org/uc/item/7n01v4f9#main},
year = {2023},
date = {2023-08-01},
journal = {Proceedings of the Annual Meeting of the Cognitive Science Society},
volume = {Volume 45},
abstract = {Women and minorities obtain lower salaries when negotiating their employment compensation. Some have suggested that automated negotiation and dispute-resolution technology might address such material inequities. These algorithms elicit the multi-criteria preferences of each side of a dispute and arrive at solutions that are efficient and "provably" fair. In a study that explores the potential benefit of these methods, we highlight cognitive factors that may allow inequities to persist despite these methods. Specifically, risk-averse individuals express lower preferences for salary and as risk-aversion is more common in women and minorities, this translates into a ``provably'' fair lower salary. While this may reflect actual underlying differences in preferences across groups, individuals may be confounding their preferences for salary with their risk preference (i.e., their fear of not reaching an agreement), such that these groups achieve worse outcomes than they should. We further highlight that methodological choices in how negotiation processes are often studied can obscure the magnitude of this effect.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Women and minorities obtain lower salaries when negotiating their employment compensation. Some have suggested that automated negotiation and dispute-resolution technology might address such material inequities. These algorithms elicit the multi-criteria preferences of each side of a dispute and arrive at solutions that are efficient and "provably" fair. In a study that explores the potential benefit of these methods, we highlight cognitive factors that may allow inequities to persist despite these methods. Specifically, risk-averse individuals express lower preferences for salary and as risk-aversion is more common in women and minorities, this translates into a ``provably'' fair lower salary. While this may reflect actual underlying differences in preferences across groups, individuals may be confounding their preferences for salary with their risk preference (i.e., their fear of not reaching an agreement), such that these groups achieve worse outcomes than they should. We further highlight that methodological choices in how negotiation processes are often studied can obscure the magnitude of this effect.
44.
Hartholt, Arno; Mozgai, Sharon
Creating Virtual Worlds with the Virtual Human Toolkit and the Rapid Integration & Development Environment Proceedings Article
In: Intelligent Human Systems Integration (IHSI 2023): Integrating People and Intelligent Systems, AHFE Open Acces, 2023, ISBN: 978-1-958651-45-2, (ISSN: 27710718 Issue: 69).
@inproceedings{hartholt_creating_2023,
title = {Creating Virtual Worlds with the Virtual Human Toolkit and the Rapid Integration & Development Environment},
author = {Arno Hartholt and Sharon Mozgai},
url = {https://openaccess.cms-conferences.org/publications/book/978-1-958651-45-2/article/978-1-958651-45-2_41},
doi = {10.54941/ahfe1002856},
isbn = {978-1-958651-45-2},
year = {2023},
date = {2023-07-01},
urldate = {2023-03-31},
booktitle = {Intelligent Human Systems Integration (IHSI 2023): Integrating People and Intelligent Systems},
volume = {69},
publisher = {AHFE Open Acces},
abstract = {The research and development of virtual humans, and the virtual worlds they inhabit, is inherently complex, requiring interdisciplinary approaches that combine social sciences, computer science, design, art, production, and domain expertise. Our previous work in managing this complexity has resulted in the release of the Virtual Human Toolkit (VHToolkit), aimed at lowering the burden of creating embodied conversational agents. In our current efforts, we are integrating the VHToolkit with the Rapid Integration & Development Environment (RIDE), a rapid prototyping modeling and simulation middleware platform that leverages real-time game engines. This integration results in the ability to mix and match commercial AI services from AWS, Azure, and Google, as well as leverage novel 3D geospatial terrain creation pipelines. Combined with dedicated authoring tools that have been developed through human-centered design processes, the platform enables researchers, developers, and domain experts to rapidly create digital worlds with virtual humans for both military and civilian contexts. Our approach is highly interdisciplinary, including academia, government, and industry collaborators. The demonstration shows a user interacting with an embodied conversational agent embedded within real-world captured and virtualized terrain. Further research and development features of the platform are shown, including scripted agent behaviors, networked team play, and machine learning interfaces.},
note = {ISSN: 27710718
Issue: 69},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The research and development of virtual humans, and the virtual worlds they inhabit, is inherently complex, requiring interdisciplinary approaches that combine social sciences, computer science, design, art, production, and domain expertise. Our previous work in managing this complexity has resulted in the release of the Virtual Human Toolkit (VHToolkit), aimed at lowering the burden of creating embodied conversational agents. In our current efforts, we are integrating the VHToolkit with the Rapid Integration & Development Environment (RIDE), a rapid prototyping modeling and simulation middleware platform that leverages real-time game engines. This integration results in the ability to mix and match commercial AI services from AWS, Azure, and Google, as well as leverage novel 3D geospatial terrain creation pipelines. Combined with dedicated authoring tools that have been developed through human-centered design processes, the platform enables researchers, developers, and domain experts to rapidly create digital worlds with virtual humans for both military and civilian contexts. Our approach is highly interdisciplinary, including academia, government, and industry collaborators. The demonstration shows a user interacting with an embodied conversational agent embedded within real-world captured and virtualized terrain. Further research and development features of the platform are shown, including scripted agent behaviors, networked team play, and machine learning interfaces.
45.
Tak, Ala N.; Gratch, Jonathan
Is GPT a Computational Model of Emotion? Detailed Analysis Journal Article
In: 2023, (Publisher: arXiv Version Number: 1).
@article{tak_is_2023,
title = {Is GPT a Computational Model of Emotion? Detailed Analysis},
author = {Ala N. Tak and Jonathan Gratch},
url = {https://arxiv.org/abs/2307.13779},
doi = {10.48550/ARXIV.2307.13779},
year = {2023},
date = {2023-07-01},
urldate = {2023-09-20},
abstract = {This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering, it is shown that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses. GPT-4 showed the highest performance in the initial study but fell short in the second, despite providing superior results after minor prompt engineering. This assessment brings up questions on how to effectively employ the strong points and address the weak areas of these models, particularly concerning response variability. These studies underscore the merits of evaluating models from a componential perspective.},
note = {Publisher: arXiv
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering, it is shown that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses. GPT-4 showed the highest performance in the initial study but fell short in the second, despite providing superior results after minor prompt engineering. This assessment brings up questions on how to effectively employ the strong points and address the weak areas of these models, particularly concerning response variability. These studies underscore the merits of evaluating models from a componential perspective.
46.
Sato, Motoaki; Terada, Kazunori; Gratch, Jonathan
Teaching Reverse Appraisal to Improve Negotiation Skills Journal Article
In: IEEE Trans. Affective Comput., pp. 1–14, 2023, ISSN: 1949-3045, 2371-9850.
@article{sato_teaching_2023,
title = {Teaching Reverse Appraisal to Improve Negotiation Skills},
author = {Motoaki Sato and Kazunori Terada and Jonathan Gratch},
url = {https://ieeexplore.ieee.org/document/10189838/},
doi = {10.1109/TAFFC.2023.3285931},
issn = {1949-3045, 2371-9850},
year = {2023},
date = {2023-07-01},
urldate = {2023-09-20},
journal = {IEEE Trans. Affective Comput.},
pages = {1–14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
47.
Wang, Ning; Karpurapu, Abhilash; Jajodia, Aditya; Merchant, Chirag
The Relationship Between Pauses and Emphasis: Implications for Charismatic Speech Synthesis Book Section
In: Kurosu, Masaaki; Hashizume, Ayako (Ed.): Human-Computer Interaction, vol. 14013, pp. 407–418, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-35601-8 978-3-031-35602-5, (Series Title: Lecture Notes in Computer Science).
@incollection{kurosu_relationship_2023,
title = {The Relationship Between Pauses and Emphasis: Implications for Charismatic Speech Synthesis},
author = {Ning Wang and Abhilash Karpurapu and Aditya Jajodia and Chirag Merchant},
editor = {Masaaki Kurosu and Ayako Hashizume},
url = {https://link.springer.com/10.1007/978-3-031-35602-5_29},
doi = {10.1007/978-3-031-35602-5_29},
isbn = {978-3-031-35601-8 978-3-031-35602-5},
year = {2023},
date = {2023-07-01},
urldate = {2023-09-20},
booktitle = {Human-Computer Interaction},
volume = {14013},
pages = {407–418},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
48.
Wang, Ning; Pynadath, David V.; Gurney, Nikolos
The Design of Transparency Communication for Human-Multirobot Teams Book Section
In: Degen, Helmut; Ntoa, Stavroula (Ed.): Artificial Intelligence in HCI, vol. 14051, pp. 311–321, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-35893-7 978-3-031-35894-4, (Series Title: Lecture Notes in Computer Science).
@incollection{degen_design_2023,
title = {The Design of Transparency Communication for Human-Multirobot Teams},
author = {Ning Wang and David V. Pynadath and Nikolos Gurney},
editor = {Helmut Degen and Stavroula Ntoa},
url = {https://link.springer.com/10.1007/978-3-031-35894-4_23},
doi = {10.1007/978-3-031-35894-4_23},
isbn = {978-3-031-35893-7 978-3-031-35894-4},
year = {2023},
date = {2023-07-01},
urldate = {2023-08-24},
booktitle = {Artificial Intelligence in HCI},
volume = {14051},
pages = {311–321},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
49.
Rodrigues, Patrick B.; Singh, Rashmi; Oytun, Mert; Adami, Pooya; Woods, Peter J.; Becerik-Gerber, Burcin; Soibelman, Lucio; Copur-Gencturk, Yasemin; Lucas, Gale M.
A multidimensional taxonomy for human-robot interaction in construction Journal Article
In: Automation in Construction, vol. 150, pp. 104845, 2023, ISSN: 0926-5805.
@article{rodrigues_multidimensional_2023,
title = {A multidimensional taxonomy for human-robot interaction in construction},
author = {Patrick B. Rodrigues and Rashmi Singh and Mert Oytun and Pooya Adami and Peter J. Woods and Burcin Becerik-Gerber and Lucio Soibelman and Yasemin Copur-Gencturk and Gale M. Lucas},
url = {https://www.sciencedirect.com/science/article/pii/S092658052300105X},
doi = {10.1016/j.autcon.2023.104845},
issn = {0926-5805},
year = {2023},
date = {2023-06-01},
urldate = {2023-03-31},
journal = {Automation in Construction},
volume = {150},
pages = {104845},
abstract = {Despite the increased interest in construction robotics both in academia and the industry, insufficient attention has been given to aspects related to Human-Robot Interaction (HRI). Characterizing HRI for construction tasks can help researchers organize knowledge in a structured manner that allows for classifying construction robotics applications and comparing and benchmarking different studies. This paper builds upon existing taxonomies and empirical studies in HRI in various industries (e.g., construction, manufacturing, and military, among others) to propose a multidimensional taxonomy to characterize HRI applications in the construction industry. The taxonomy design followed a systematic literature review in which common themes were identified and grouped into 16 categories. The proposed taxonomy can be used as a foundation for systematic reviews and meta-analyses of HRI applications in construction and can benefit the construction industry by informing the design of collaborative tasks performed by human-robot teams.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Despite the increased interest in construction robotics both in academia and the industry, insufficient attention has been given to aspects related to Human-Robot Interaction (HRI). Characterizing HRI for construction tasks can help researchers organize knowledge in a structured manner that allows for classifying construction robotics applications and comparing and benchmarking different studies. This paper builds upon existing taxonomies and empirical studies in HRI in various industries (e.g., construction, manufacturing, and military, among others) to propose a multidimensional taxonomy to characterize HRI applications in the construction industry. The taxonomy design followed a systematic literature review in which common themes were identified and grouped into 16 categories. The proposed taxonomy can be used as a foundation for systematic reviews and meta-analyses of HRI applications in construction and can benefit the construction industry by informing the design of collaborative tasks performed by human-robot teams.
50.
Johnson, Emmanuel; Gratch, Jonathan; Gil, Yolanda
Virtual Agent Approach for Teaching the Collaborative Problem Solving Skill of Negotiation Book Section
In: Wang, Ning; Rebolledo-Mendez, Genaro; Dimitrova, Vania; Matsuda, Noboru; Santos, Olga C. (Ed.): Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, vol. 1831, pp. 530–535, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-36335-1 978-3-031-36336-8, (Series Title: Communications in Computer and Information Science).
@incollection{wang_virtual_2023,
title = {Virtual Agent Approach for Teaching the Collaborative Problem Solving Skill of Negotiation},
author = {Emmanuel Johnson and Jonathan Gratch and Yolanda Gil},
editor = {Ning Wang and Genaro Rebolledo-Mendez and Vania Dimitrova and Noboru Matsuda and Olga C. Santos},
url = {https://link.springer.com/10.1007/978-3-031-36336-8_82},
doi = {10.1007/978-3-031-36336-8_82},
isbn = {978-3-031-36335-1 978-3-031-36336-8},
year = {2023},
date = {2023-06-01},
urldate = {2023-09-20},
booktitle = {Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky},
volume = {1831},
pages = {530–535},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Communications in Computer and Information Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
51.
Leitner, Maxyn; Greenwald, Eric; Wang, Ning; Montgomery, Ryan; Merchant, Chirag
Designing Game-Based Learning for High School Artificial Intelligence Education Journal Article
In: Int J Artif Intell Educ, vol. 33, no. 2, pp. 384–398, 2023, ISSN: 1560-4292, 1560-4306.
@article{leitner_designing_2023,
title = {Designing Game-Based Learning for High School Artificial Intelligence Education},
author = {Maxyn Leitner and Eric Greenwald and Ning Wang and Ryan Montgomery and Chirag Merchant},
url = {https://link.springer.com/10.1007/s40593-022-00327-w},
doi = {10.1007/s40593-022-00327-w},
issn = {1560-4292, 1560-4306},
year = {2023},
date = {2023-06-01},
urldate = {2023-09-20},
journal = {Int J Artif Intell Educ},
volume = {33},
number = {2},
pages = {384–398},
abstract = {Abstract
Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI to even younger learners. In this theoretical paper, we discuss the design of a game-based learning environment for high school AI education, drawing upon insights gained from a prior cognitive interview study at a STEM focused private high school. We argue that game-based learning is an excellent fit for AI education due to the commonality of problem solving in both game playing and AI.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI to even younger learners. In this theoretical paper, we discuss the design of a game-based learning environment for high school AI education, drawing upon insights gained from a prior cognitive interview study at a STEM focused private high school. We argue that game-based learning is an excellent fit for AI education due to the commonality of problem solving in both game playing and AI.
Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI to even younger learners. In this theoretical paper, we discuss the design of a game-based learning environment for high school AI education, drawing upon insights gained from a prior cognitive interview study at a STEM focused private high school. We argue that game-based learning is an excellent fit for AI education due to the commonality of problem solving in both game playing and AI.
52.
Okado, Yuko; Nye, Benjamin D.; Aguirre, Angelica; Swartout, William
In: Wang, Ning; Rebolledo-Mendez, Genaro; Matsuda, Noboru; Santos, Olga C.; Dimitrova, Vania (Ed.): Artificial Intelligence in Education, vol. 13916, pp. 189–201, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-36271-2 978-3-031-36272-9, (Series Title: Lecture Notes in Computer Science).
@incollection{wang_can_2023,
title = {Can Virtual Agents Scale Up Mentoring?: Insights from College Students’ Experiences Using the CareerFair.ai Platform at an American Hispanic-Serving Institution},
author = {Yuko Okado and Benjamin D. Nye and Angelica Aguirre and William Swartout},
editor = {Ning Wang and Genaro Rebolledo-Mendez and Noboru Matsuda and Olga C. Santos and Vania Dimitrova},
url = {https://link.springer.com/10.1007/978-3-031-36272-9_16},
doi = {10.1007/978-3-031-36272-9_16},
isbn = {978-3-031-36271-2 978-3-031-36272-9},
year = {2023},
date = {2023-06-01},
urldate = {2023-08-23},
booktitle = {Artificial Intelligence in Education},
volume = {13916},
pages = {189–201},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
53.
Tran, Minh; Soleymani, Mohammad
A Speech Representation Anonymization Framework via Selective Noise Perturbation Proceedings Article
In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5, IEEE, Rhodes Island, Greece, 2023, ISBN: 978-1-7281-6327-7.
@inproceedings{tran_speech_2023,
title = {A Speech Representation Anonymization Framework via Selective Noise Perturbation},
author = {Minh Tran and Mohammad Soleymani},
url = {https://ieeexplore.ieee.org/document/10095173/},
doi = {10.1109/ICASSP49357.2023.10095173},
isbn = {978-1-7281-6327-7},
year = {2023},
date = {2023-06-01},
urldate = {2023-08-23},
booktitle = {ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1–5},
publisher = {IEEE},
address = {Rhodes Island, Greece},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
54.
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning Journal Article
In: FLAIRS, vol. 36, 2023, ISSN: 2334-0762.
@article{aris_learning_2023,
title = {Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/133348},
doi = {10.32473/flairs.36.133348},
issn = {2334-0762},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-04},
journal = {FLAIRS},
volume = {36},
abstract = {This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. This work expands on previous work on raycast-based agents by increasing the number of enemies from one to three. We demonstrate an automated way of generating training and testing data within geo-specific terrains. We show that replacing the action space with a more abstracted, navmesh-based waypoint movement system can increase the generality and success rate of the models while providing similar results to our previous paper's results regarding retraining across terrains. We also comprehensively evaluate the differences between these and the previous models. Finally, we show that incorporating pixels into the model's input can increase performance at the cost of longer training times.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. This work expands on previous work on raycast-based agents by increasing the number of enemies from one to three. We demonstrate an automated way of generating training and testing data within geo-specific terrains. We show that replacing the action space with a more abstracted, navmesh-based waypoint movement system can increase the generality and success rate of the models while providing similar results to our previous paper's results regarding retraining across terrains. We also comprehensively evaluate the differences between these and the previous models. Finally, we show that incorporating pixels into the model's input can increase performance at the cost of longer training times.
55.
Murawski, Alaine; Ramirez-Zohfeld, Vanessa; Schierer, Allison; Olvera, Charles; Mell, Johnathan; Gratch, Jonathan; Brett, Jeanne; Lindquist, Lee A.
Transforming a Negotiation Framework to Resolve Conflicts among Older Adults and Family Caregivers Journal Article
In: Geriatrics, vol. 8, no. 2, pp. 36, 2023, ISSN: 2308-3417, (Number: 2 Publisher: Multidisciplinary Digital Publishing Institute).
@article{murawski_transforming_2023,
title = {Transforming a Negotiation Framework to Resolve Conflicts among Older Adults and Family Caregivers},
author = {Alaine Murawski and Vanessa Ramirez-Zohfeld and Allison Schierer and Charles Olvera and Johnathan Mell and Jonathan Gratch and Jeanne Brett and Lee A. Lindquist},
url = {https://www.mdpi.com/2308-3417/8/2/36},
doi = {10.3390/geriatrics8020036},
issn = {2308-3417},
year = {2023},
date = {2023-04-01},
urldate = {2023-03-31},
journal = {Geriatrics},
volume = {8},
number = {2},
pages = {36},
abstract = {Background: Family caregivers of older people with Alzheimer’s dementia (PWD) often need to advocate and resolve health-related conflicts (e.g., determining treatment necessity, billing errors, and home health extensions). As they deal with these health system conflicts, family caregivers experience unnecessary frustration, anxiety, and stress. The goal of this research was to apply a negotiation framework to resolve real-world family caregiver–older adult conflicts. Methods: We convened an interdisciplinary team of national community-based family caregivers, social workers, geriatricians, and negotiation experts (n = 9; Illinois, Florida, New York, and California) to examine the applicability of negotiation and conflict management frameworks to three older adult–caregiver conflicts (i.e., caregiver–older adult, caregiver–provider, and caregiver–caregiver). The panel of caregivers provided scenarios and dialogue describing conflicts they experienced in these three settings. A qualitative analysis was then performed grouping the responses into a framework matrix. Results: Upon presenting the three conflicts to the caregivers, 96 responses (caregiver–senior), 75 responses (caregiver–caregiver), and 80 responses (caregiver–provider) were generated. A thematic analysis showed that the statements and responses fit the interest–rights–power (IRP) negotiation framework. Discussion: The interests–rights–power (IRP) framework, used in business negotiations, provided insight into how caregivers experienced conflict with older adults, providers, and other caregivers. Future research is needed to examine applying the IRP framework in the training of caregivers of older people with Alzheimer’s dementia.},
note = {Number: 2
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Family caregivers of older people with Alzheimer’s dementia (PWD) often need to advocate and resolve health-related conflicts (e.g., determining treatment necessity, billing errors, and home health extensions). As they deal with these health system conflicts, family caregivers experience unnecessary frustration, anxiety, and stress. The goal of this research was to apply a negotiation framework to resolve real-world family caregiver–older adult conflicts. Methods: We convened an interdisciplinary team of national community-based family caregivers, social workers, geriatricians, and negotiation experts (n = 9; Illinois, Florida, New York, and California) to examine the applicability of negotiation and conflict management frameworks to three older adult–caregiver conflicts (i.e., caregiver–older adult, caregiver–provider, and caregiver–caregiver). The panel of caregivers provided scenarios and dialogue describing conflicts they experienced in these three settings. A qualitative analysis was then performed grouping the responses into a framework matrix. Results: Upon presenting the three conflicts to the caregivers, 96 responses (caregiver–senior), 75 responses (caregiver–caregiver), and 80 responses (caregiver–provider) were generated. A thematic analysis showed that the statements and responses fit the interest–rights–power (IRP) negotiation framework. Discussion: The interests–rights–power (IRP) framework, used in business negotiations, provided insight into how caregivers experienced conflict with older adults, providers, and other caregivers. Future research is needed to examine applying the IRP framework in the training of caregivers of older people with Alzheimer’s dementia.
56.
Melo, Celso M. De; Gratch, Jonathan; Marsella, Stacy; Pelachaud, Catherine
Social Functions of Machine Emotional Expressions Journal Article
In: Proc. IEEE, pp. 1–16, 2023, ISSN: 0018-9219, 1558-2256.
@article{de_melo_social_2023,
title = {Social Functions of Machine Emotional Expressions},
author = {Celso M. De Melo and Jonathan Gratch and Stacy Marsella and Catherine Pelachaud},
url = {https://ieeexplore.ieee.org/document/10093227/},
doi = {10.1109/JPROC.2023.3261137},
issn = {0018-9219, 1558-2256},
year = {2023},
date = {2023-04-01},
urldate = {2023-08-04},
journal = {Proc. IEEE},
pages = {1–16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
57.
Gratch, Jonathan
The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective Journal Article
In: Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 378, no. 1875, pp. 20210475, 2023, (Publisher: Royal Society).
@article{gratch_promise_2023,
title = {The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective},
author = {Jonathan Gratch},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rstb.2021.0475},
doi = {10.1098/rstb.2021.0475},
year = {2023},
date = {2023-03-01},
urldate = {2023-03-31},
journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
volume = {378},
number = {1875},
pages = {20210475},
abstract = {In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and expressions. Any science of face-to-face interaction must develop approaches to hypothesize and rigorously test mechanisms that explain such interdependent behaviour. Yet conventional experimental designs often sacrifice interactivity to establish experimental control. Interactive virtual and robotic agents have been offered as a way to study true interactivity while enforcing a measure of experimental control by allowing participants to interact with realistic but carefully controlled partners. But as researchers increasingly turn to machine learning to add realism to such agents, they may unintentionally distort the very interactivity they seek to illuminate, particularly when investigating the role of non-verbal signals such as emotion or active-listening behaviours. Here I discuss some of the methodological challenges that may arise when machine learning is used to model the behaviour of interaction partners. By articulating and explicitly considering these commitments, researchers can transform ‘unintentional distortions’ into valuable methodological tools that yield new insights and better contextualize existing experimental findings that rely on learning technology.
This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.},
note = {Publisher: Royal Society},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and expressions. Any science of face-to-face interaction must develop approaches to hypothesize and rigorously test mechanisms that explain such interdependent behaviour. Yet conventional experimental designs often sacrifice interactivity to establish experimental control. Interactive virtual and robotic agents have been offered as a way to study true interactivity while enforcing a measure of experimental control by allowing participants to interact with realistic but carefully controlled partners. But as researchers increasingly turn to machine learning to add realism to such agents, they may unintentionally distort the very interactivity they seek to illuminate, particularly when investigating the role of non-verbal signals such as emotion or active-listening behaviours. Here I discuss some of the methodological challenges that may arise when machine learning is used to model the behaviour of interaction partners. By articulating and explicitly considering these commitments, researchers can transform ‘unintentional distortions’ into valuable methodological tools that yield new insights and better contextualize existing experimental findings that rely on learning technology.
This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
58.
Georgila, Kallirroi
Considerations for Child Speech Synthesis for Dialogue Systems Proceedings Article
In: Los Angeles, CA, 2023.
@inproceedings{georgila_considerations_2023,
title = {Considerations for Child Speech Synthesis for Dialogue Systems},
author = {Kallirroi Georgila},
url = {https://kgeorgila.github.io/publications/georgila_aiaic23.pdf},
year = {2023},
date = {2023-03-01},
address = {Los Angeles, CA},
abstract = {We present a number of important issues for consideration with regard to child speech synthesis for dialogue systems. We specifically discuss challenges in building child synthetic voices compared to adult synthetic voices, synthesizing expressive conversational speech, and evaluating speech synthesis quality.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a number of important issues for consideration with regard to child speech synthesis for dialogue systems. We specifically discuss challenges in building child synthetic voices compared to adult synthetic voices, synthesizing expressive conversational speech, and evaluating speech synthesis quality.
59.
Lu, Shuhong; Yoon, Youngwoo; Feng, Andrew
Co-Speech Gesture Synthesis using Discrete Gesture Token Learning Journal Article
In: 2023, (Publisher: arXiv Version Number: 1).
@article{lu_co-speech_2023,
title = {Co-Speech Gesture Synthesis using Discrete Gesture Token Learning},
author = {Shuhong Lu and Youngwoo Yoon and Andrew Feng},
url = {https://arxiv.org/abs/2303.12822},
doi = {10.48550/ARXIV.2303.12822},
year = {2023},
date = {2023-03-01},
urldate = {2023-08-04},
abstract = {Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robots by human users and will find applications in education, training, and medical services. One challenge in learning the co-speech gesture model is that there may be multiple viable gesture motions for the same speech utterance. The deterministic regression methods can not resolve the conflicting samples and may produce over-smoothed or damped motions. We proposed a two-stage model to address this uncertainty issue in gesture synthesis by modeling the gesture segments as discrete latent codes. Our method utilizes RQ-VAE in the first stage to learn a discrete codebook consisting of gesture tokens from training data. In the second stage, a two-level autoregressive transformer model is used to learn the prior distribution of residual codes conditioned on input speech context. Since the inference is formulated as token sampling, multiple gesture sequences could be generated given the same speech input using top-k sampling. The quantitative results and the user study showed the proposed method outperforms the previous methods and is able to generate realistic and diverse gesture motions.},
note = {Publisher: arXiv
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robots by human users and will find applications in education, training, and medical services. One challenge in learning the co-speech gesture model is that there may be multiple viable gesture motions for the same speech utterance. The deterministic regression methods can not resolve the conflicting samples and may produce over-smoothed or damped motions. We proposed a two-stage model to address this uncertainty issue in gesture synthesis by modeling the gesture segments as discrete latent codes. Our method utilizes RQ-VAE in the first stage to learn a discrete codebook consisting of gesture tokens from training data. In the second stage, a two-level autoregressive transformer model is used to learn the prior distribution of residual codes conditioned on input speech context. Since the inference is formulated as token sampling, multiple gesture sequences could be generated given the same speech input using top-k sampling. The quantitative results and the user study showed the proposed method outperforms the previous methods and is able to generate realistic and diverse gesture motions.
60.
Awada, Mohamad; Becerik-Gerber, Burcin; Liu, Ruying; Seyedrezaei, Mirmahdi; Lu, Zheng; Xenakis, Matheos; Lucas, Gale; Roll, Shawn C.; Narayanan, Shrikanth
Ten questions concerning the impact of environmental stress on office workers Journal Article
In: Building and Environment, vol. 229, pp. 109964, 2023, ISSN: 0360-1323.
@article{awada_ten_2023,
title = {Ten questions concerning the impact of environmental stress on office workers},
author = {Mohamad Awada and Burcin Becerik-Gerber and Ruying Liu and Mirmahdi Seyedrezaei and Zheng Lu and Matheos Xenakis and Gale Lucas and Shawn C. Roll and Shrikanth Narayanan},
url = {https://www.sciencedirect.com/science/article/pii/S0360132322011945},
doi = {10.1016/j.buildenv.2022.109964},
issn = {0360-1323},
year = {2023},
date = {2023-02-01},
urldate = {2023-03-31},
journal = {Building and Environment},
volume = {229},
pages = {109964},
abstract = {We regularly face stress during our everyday activities, to the extent that stress is recognized by the World Health Organization as the epidemic of the 21st century. Stress is how humans respond physically and psychologically to adjustments, experiences, conditions, and circumstances in their lives. While there are many reasons for stress, work and job pressure remain the main cause. Thus, companies are increasingly interested in creating healthier, more comfortable, and stress-free offices for their workers. The indoor environment can induce environmental stress when it cannot satisfy the individual needs for health and comfort. In fact, office environmental conditions (e.g., thermal, and indoor air conditions, lighting, and noise) and interior design parameters (e.g., office layout, colors, furniture, access to views, distance to window, personal control and biophilic design) have been found to affect office workers' stress levels. A line of research based on the stress recovery theory offers new insights for establishing offices that limit environmental stress and help with work stress recovery. To that end, this paper answers ten questions that explore the relation between the indoor office-built environment and stress levels among workers. The answers to the ten questions are based on an extensive literature review to draw conclusions from what has been achieved to date. Thus, this study presents a foundation for future environmental stress related research in offices.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We regularly face stress during our everyday activities, to the extent that stress is recognized by the World Health Organization as the epidemic of the 21st century. Stress is how humans respond physically and psychologically to adjustments, experiences, conditions, and circumstances in their lives. While there are many reasons for stress, work and job pressure remain the main cause. Thus, companies are increasingly interested in creating healthier, more comfortable, and stress-free offices for their workers. The indoor environment can induce environmental stress when it cannot satisfy the individual needs for health and comfort. In fact, office environmental conditions (e.g., thermal, and indoor air conditions, lighting, and noise) and interior design parameters (e.g., office layout, colors, furniture, access to views, distance to window, personal control and biophilic design) have been found to affect office workers' stress levels. A line of research based on the stress recovery theory offers new insights for establishing offices that limit environmental stress and help with work stress recovery. To that end, this paper answers ten questions that explore the relation between the indoor office-built environment and stress levels among workers. The answers to the ten questions are based on an extensive literature review to draw conclusions from what has been achieved to date. Thus, this study presents a foundation for future environmental stress related research in offices.
Filter
2021
Terada, Kazunori; Okazoe, Mitsuki; Gratch, Jonathan
Effect of politeness strategies in dialogue on negotiation outcomes Proceedings Article
In: Proceedings of the 21th ACM International Conference on Intelligent Virtual Agents, pp. 195–202, ACM, Virtual Event Japan, 2021, ISBN: 978-1-4503-8619-7.
Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@inproceedings{terada_effect_2021,
title = {Effect of politeness strategies in dialogue on negotiation outcomes},
author = {Kazunori Terada and Mitsuki Okazoe and Jonathan Gratch},
url = {https://dl.acm.org/doi/10.1145/3472306.3478336},
doi = {10.1145/3472306.3478336},
isbn = {978-1-4503-8619-7},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-28},
booktitle = {Proceedings of the 21th ACM International Conference on Intelligent Virtual Agents},
pages = {195–202},
publisher = {ACM},
address = {Virtual Event Japan},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Johnson, Emmanuel; Gratch, Jonathan; Boberg, Jill; DeVault, David; Kim, Peter; Lucas, Gale
Using Intelligent Agents to Examine Gender in Negotiations Proceedings Article
In: Proceedings of the 21th ACM International Conference on Intelligent Virtual Agents, pp. 90–97, ACM, Virtual Event Japan, 2021, ISBN: 978-1-4503-8619-7.
Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@inproceedings{johnson_using_2021,
title = {Using Intelligent Agents to Examine Gender in Negotiations},
author = {Emmanuel Johnson and Jonathan Gratch and Jill Boberg and David DeVault and Peter Kim and Gale Lucas},
url = {https://dl.acm.org/doi/10.1145/3472306.3478348},
doi = {10.1145/3472306.3478348},
isbn = {978-1-4503-8619-7},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-28},
booktitle = {Proceedings of the 21th ACM International Conference on Intelligent Virtual Agents},
pages = {90–97},
publisher = {ACM},
address = {Virtual Event Japan},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Johnson, Emmanuel; Gratch, Jonathan
Comparing The Accuracy of Frequentist and Bayesian Models in Human-Agent Negotiation Proceedings Article
In: Proceedings of the 21th ACM International Conference on Intelligent Virtual Agents, pp. 139–144, ACM, Virtual Event Japan, 2021, ISBN: 978-1-4503-8619-7.
Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@inproceedings{johnson_comparing_2021,
title = {Comparing The Accuracy of Frequentist and Bayesian Models in Human-Agent Negotiation},
author = {Emmanuel Johnson and Jonathan Gratch},
url = {https://dl.acm.org/doi/10.1145/3472306.3478354},
doi = {10.1145/3472306.3478354},
isbn = {978-1-4503-8619-7},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-28},
booktitle = {Proceedings of the 21th ACM International Conference on Intelligent Virtual Agents},
pages = {139–144},
publisher = {ACM},
address = {Virtual Event Japan},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Sato, Motoaki; Terada, Kazunori; Gratch, Jonathan
Visualization of social emotional appraisal process of an agent Proceedings Article
In: 2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 1–2, IEEE, Nara, Japan, 2021, ISBN: 978-1-6654-0021-3.
Links | BibTeX | Tags: Emotions, Virtual Humans
@inproceedings{sato_visualization_2021,
title = {Visualization of social emotional appraisal process of an agent},
author = {Motoaki Sato and Kazunori Terada and Jonathan Gratch},
url = {https://ieeexplore.ieee.org/document/9666329/},
doi = {10.1109/ACIIW52867.2021.9666329},
isbn = {978-1-6654-0021-3},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-28},
booktitle = {2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)},
pages = {1–2},
publisher = {IEEE},
address = {Nara, Japan},
keywords = {Emotions, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
et al A Rizzo,
Normative Data for a Next Generation Virtual Classroom for Attention Assessment in Children with ADHD and Beyond! Proceedings Article
In: Proceedings of the 13th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT 2021), Serpa, Portugal, 2021.
Links | BibTeX | Tags: MedVR, Virtual Humans, VR
@inproceedings{a_rizzo_et_al_normative_2021,
title = {Normative Data for a Next Generation Virtual Classroom for Attention Assessment in Children with ADHD and Beyond!},
author = {et al A Rizzo},
url = {http://studio.hei-lab.ulusofona.pt/archive/},
year = {2021},
date = {2021-09-01},
booktitle = {Proceedings of the 13th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT 2021)},
address = {Serpa, Portugal},
keywords = {MedVR, Virtual Humans, VR},
pubstate = {published},
tppubtype = {inproceedings}
}
Hartholt, Arno; Fast, Ed; Leeds, Andrew; Mozgai, Sharon
Introducing VHMason: A Visual, Integrated, Multimodal Virtual Human Authoring Tool Proceedings Article
In: Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents, pp. 109–111, Association for Computing Machinery, New York, NY, USA, 2021, ISBN: 978-1-4503-8619-7.
Abstract | Links | BibTeX | Tags: UARC, VHTL, Virtual Humans
@inproceedings{hartholt_introducing_2021-1,
title = {Introducing VHMason: A Visual, Integrated, Multimodal Virtual Human Authoring Tool},
author = {Arno Hartholt and Ed Fast and Andrew Leeds and Sharon Mozgai},
url = {https://dl.acm.org/doi/10.1145/3472306.3478363},
doi = {10.1145/3472306.3478363},
isbn = {978-1-4503-8619-7},
year = {2021},
date = {2021-09-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents},
pages = {109–111},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {IVA '21},
abstract = {A major impediment to the success of virtual agents is the inability of non-technical experts to easily author content. To address this barrier we present VHMason, a multimodal authoring tool designed to help creative authors build embodied conversational agents. We introduce the novel aspects of this authoring tool and explore a use case of the creation of an agent-led educational experience implemented at Children's Hospital Los Angeles (CHLA).},
keywords = {UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
A major impediment to the success of virtual agents is the inability of non-technical experts to easily author content. To address this barrier we present VHMason, a multimodal authoring tool designed to help creative authors build embodied conversational agents. We introduce the novel aspects of this authoring tool and explore a use case of the creation of an agent-led educational experience implemented at Children's Hospital Los Angeles (CHLA).
Rizzo, Albert “Skip”; Hartholt, Arno; Mozgai, Sharon
From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality Journal Article
In: Journal of Technology in Human Services, vol. 39, no. 3, pp. 314–347, 2021, ISSN: 1522-8835, (Publisher: Routledge _eprint: https://doi.org/10.1080/15228835.2021.1915931).
Abstract | Links | BibTeX | Tags: MedVR, UARC, VHTL, Virtual Humans
@article{rizzo_combat_2021,
title = {From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality},
author = {Albert “Skip” Rizzo and Arno Hartholt and Sharon Mozgai},
url = {https://doi.org/10.1080/15228835.2021.1915931},
doi = {10.1080/15228835.2021.1915931},
issn = {1522-8835},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {Journal of Technology in Human Services},
volume = {39},
number = {3},
pages = {314–347},
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.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/15228835.2021.1915931},
keywords = {MedVR, UARC, VHTL, Virtual Humans},
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.
Horstmann, Aike C.; Gratch, Jonathan; Krämer, Nicole C.
I Just Wanna Blame Somebody, Not Something! Reactions to a Computer Agent Giving Negative Feedback Based on the Instructions of a Person Journal Article
In: International Journal of Human-Computer Studies, pp. 102683, 2021, ISSN: 10715819.
Abstract | Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@article{horstmann_i_2021,
title = {I Just Wanna Blame Somebody, Not Something! Reactions to a Computer Agent Giving Negative Feedback Based on the Instructions of a Person},
author = {Aike C. Horstmann and Jonathan Gratch and Nicole C. Krämer},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1071581921001014},
doi = {10.1016/j.ijhcs.2021.102683},
issn = {10715819},
year = {2021},
date = {2021-06-01},
urldate = {2021-06-18},
journal = {International Journal of Human-Computer Studies},
pages = {102683},
abstract = {Previous research focused on differences between interacting with a person-controlled avatar and a computer-controlled virtual agent. This study however examines an aspiring form of technology called agent representative which constitutes a mix of the former two interaction partner types since it is a computer agent which was previously instructed by a person to take over a task on the person’s behalf. In an experimental lab study with a 2 x 3 between-subjects-design (N = 195), people believed to study together either with an agent representative, avatar, or virtual agent. The interaction partner was described to either possess high or low expertise, while always giving negative feedback regarding the participant’s performance. Results show small but interesting differences regarding the type of agency. People attributed the most agency and blame to the person(s) behind the software and reported the most negative affect when interacting with an avatar, which was less the case for a person’s agent representative and the least for a virtual agent. Level of expertise had no significant effect and other evaluation measures were not affected.},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Previous research focused on differences between interacting with a person-controlled avatar and a computer-controlled virtual agent. This study however examines an aspiring form of technology called agent representative which constitutes a mix of the former two interaction partner types since it is a computer agent which was previously instructed by a person to take over a task on the person’s behalf. In an experimental lab study with a 2 x 3 between-subjects-design (N = 195), people believed to study together either with an agent representative, avatar, or virtual agent. The interaction partner was described to either possess high or low expertise, while always giving negative feedback regarding the participant’s performance. Results show small but interesting differences regarding the type of agency. People attributed the most agency and blame to the person(s) behind the software and reported the most negative affect when interacting with an avatar, which was less the case for a person’s agent representative and the least for a virtual agent. Level of expertise had no significant effect and other evaluation measures were not affected.
Mell, Johnathan; Beissinger, Markus; Gratch, Jonathan
An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied data Journal Article
In: J Multimodal User Interfaces, 2021, ISSN: 1783-7677, 1783-8738.
Abstract | Links | BibTeX | Tags: DTIC, Machine Learning, UARC, Virtual Humans
@article{mell_expert-model_2021,
title = {An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied data},
author = {Johnathan Mell and Markus Beissinger and Jonathan Gratch},
url = {http://link.springer.com/10.1007/s12193-021-00368-w},
doi = {10.1007/s12193-021-00368-w},
issn = {1783-7677, 1783-8738},
year = {2021},
date = {2021-03-01},
urldate = {2021-04-15},
journal = {J Multimodal User Interfaces},
abstract = {We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other techniques (such as linear regression models or boosted decision trees). In a follow-up study, we show that the most successful models change as the dataset size increases and the prediction targets change, and show that boosted decision trees may not be suitable for the negotiation domain. We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation. Further, we show that this approach can be a stepping stone from purely exploratory research to targeted human-behavioral experimentation. Through our approach, areas of social artificial intelligence that have historically benefited from expert knowledge and traditional AI approaches can be combined with more recent proven-effective machine learning algorithms.},
keywords = {DTIC, Machine Learning, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other techniques (such as linear regression models or boosted decision trees). In a follow-up study, we show that the most successful models change as the dataset size increases and the prediction targets change, and show that boosted decision trees may not be suitable for the negotiation domain. We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation. Further, we show that this approach can be a stepping stone from purely exploratory research to targeted human-behavioral experimentation. Through our approach, areas of social artificial intelligence that have historically benefited from expert knowledge and traditional AI approaches can be combined with more recent proven-effective machine learning algorithms.
Melo, Celso M.; Gratch, Jonathan; Krueger, Frank
Heuristic thinking and altruism toward machines in people impacted by COVID-19 Journal Article
In: iScience, vol. 24, no. 3, pp. 102228, 2021, ISSN: 25890042.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{de_melo_heuristic_2021,
title = {Heuristic thinking and altruism toward machines in people impacted by COVID-19},
author = {Celso M. Melo and Jonathan Gratch and Frank Krueger},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004221001966},
doi = {10.1016/j.isci.2021.102228},
issn = {25890042},
year = {2021},
date = {2021-03-01},
urldate = {2021-04-14},
journal = {iScience},
volume = {24},
number = {3},
pages = {102228},
abstract = {Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Journal Article
In: pp. 13, 2021.
Abstract | Links | BibTeX | Tags: ARL, Dialogue, UARC, Virtual Humans
@article{gervits_classication-based_2021,
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},
year = {2021},
date = {2021-03-01},
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 = {ARL, Dialogue, UARC, Virtual Humans},
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.
Gordon, Carla; Georgila, Kallirroi; Yanov, Volodymyr; Traum, David
Towards Personalization of Spoken Dialogue System Communication Strategies Book Section
In: D'Haro, Luis Fernando; Callejas, Zoraida; Nakamura, Satoshi (Ed.): Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 145–160, Springer Singapore, Singapore, 2021, ISBN: 9789811583940 9789811583957, (Series Title: Lecture Notes in Electrical Engineering).
Abstract | Links | BibTeX | Tags: Dialogue, Natural Language, UARC, Virtual Humans
@incollection{dharo_towards_2021,
title = {Towards Personalization of Spoken Dialogue System Communication Strategies},
author = {Carla Gordon and Kallirroi Georgila and Volodymyr Yanov and David Traum},
editor = {Luis Fernando D'Haro and Zoraida Callejas and Satoshi Nakamura},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
doi = {10.1007/978-981-15-8395-7_11},
isbn = {9789811583940 9789811583957},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {145--160},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
note = {Series Title: Lecture Notes in Electrical Engineering},
keywords = {Dialogue, Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
Brixey, Jacqueline; Traum, David
Masheli: A Choctaw-English Bilingual Chatbot Book Section
In: D'Haro, Luis Fernando; Callejas, Zoraida; Nakamura, Satoshi (Ed.): Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 41–50, Springer Singapore, Singapore, 2021, ISBN: 9789811583940 9789811583957, (Series Title: Lecture Notes in Electrical Engineering).
Abstract | Links | BibTeX | Tags: Natural Language, UARC, Virtual Humans
@incollection{dharo_masheli_2021,
title = {Masheli: A Choctaw-English Bilingual Chatbot},
author = {Jacqueline Brixey and David Traum},
editor = {Luis Fernando D'Haro and Zoraida Callejas and Satoshi Nakamura},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_4},
doi = {10.1007/978-981-15-8395-7_4},
isbn = {9789811583940 9789811583957},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {41--50},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to practice. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.},
note = {Series Title: Lecture Notes in Electrical Engineering},
keywords = {Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to practice. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.
Melo, Celso M.; Marsella, Stacy; Gratch, Jonathan
Risk of Injury in Moral Dilemmas With Autonomous Vehicles Journal Article
In: Front. Robot. AI, vol. 7, pp. 572529, 2021, ISSN: 2296-9144.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{de_melo_risk_2021,
title = {Risk of Injury in Moral Dilemmas With Autonomous Vehicles},
author = {Celso M. Melo and Stacy Marsella and Jonathan Gratch},
url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.572529/full},
doi = {10.3389/frobt.2020.572529},
issn = {2296-9144},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-14},
journal = {Front. Robot. AI},
volume = {7},
pages = {572529},
abstract = {As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e., framing decisions as life and death and neglecting the influence of risk of injury to the involved parties on the outcome. Here, we focus on this gap and present experimental work that systematically studies the effect of risk of injury on the decisions people make in these dilemmas. In four experiments, participants were asked to program their AVs to either save five pedestrians, which we refer to as the utilitarian choice, or save the driver, which we refer to as the nonutilitarian choice. The results indicate that most participants made the utilitarian choice but that this choice was moderated in important ways by perceived risk to the driver and risk to the pedestrians. As a second contribution, we demonstrate the value of formulating AV moral dilemmas in a game-theoretic framework that considers the possible influence of others’ behavior. In the fourth experiment, we show that participants were more (less) likely to make the utilitarian choice, the more utilitarian (nonutilitarian) other drivers behaved; furthermore, unlike the game-theoretic prediction that decision-makers inevitably converge to nonutilitarianism, we found significant evidence of utilitarianism. We discuss theoretical implications for our understanding of human decision-making in moral dilemmas and practical guidelines for the design of autonomous machines that solve these dilemmas while, at the same time, being likely to be adopted in practice.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e., framing decisions as life and death and neglecting the influence of risk of injury to the involved parties on the outcome. Here, we focus on this gap and present experimental work that systematically studies the effect of risk of injury on the decisions people make in these dilemmas. In four experiments, participants were asked to program their AVs to either save five pedestrians, which we refer to as the utilitarian choice, or save the driver, which we refer to as the nonutilitarian choice. The results indicate that most participants made the utilitarian choice but that this choice was moderated in important ways by perceived risk to the driver and risk to the pedestrians. As a second contribution, we demonstrate the value of formulating AV moral dilemmas in a game-theoretic framework that considers the possible influence of others’ behavior. In the fourth experiment, we show that participants were more (less) likely to make the utilitarian choice, the more utilitarian (nonutilitarian) other drivers behaved; furthermore, unlike the game-theoretic prediction that decision-makers inevitably converge to nonutilitarianism, we found significant evidence of utilitarianism. We discuss theoretical implications for our understanding of human decision-making in moral dilemmas and practical guidelines for the design of autonomous machines that solve these dilemmas while, at the same time, being likely to be adopted in practice.
Kawano, Seiya; Yoshino, Koichiro; Traum, David; Nakamura, Satoshi
Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning Proceedings Article
In: 1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction, pp. 21–29, ISCA, 2021.
Abstract | Links | BibTeX | Tags: ARL, Dialogue, DTIC, Natural Language, Virtual Humans
@inproceedings{kawano_dialogue_2021,
title = {Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning},
author = {Seiya Kawano and Koichiro Yoshino and David Traum and Satoshi Nakamura},
url = {http://www.isca-speech.org/archive/RobotDial_2021/abstracts/4.html},
doi = {10.21437/RobotDial.2021-4},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction},
pages = {21–29},
publisher = {ISCA},
abstract = {A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.},
keywords = {ARL, Dialogue, DTIC, Natural Language, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.
Gratch, Jonathan
The field of Affective Computing: An interdisciplinary perspective Journal Article
In: Transactions of the Japanese Society for Artificial Intelligence, vol. 36, no. 1, pp. 13, 2021.
Links | BibTeX | Tags: Virtual Humans
@article{gratch_field_2021,
title = {The field of Affective Computing: An interdisciplinary perspective},
author = {Jonathan Gratch},
url = {https://people.ict.usc.edu/~gratch/CSCI534/Readings/Gratch%20-%20The%20field%20of%20affective%20computing.pdf},
year = {2021},
date = {2021-01-01},
journal = {Transactions of the Japanese Society for Artificial Intelligence},
volume = {36},
number = {1},
pages = {13},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Lee, Minha; Lucas, Gale; Gratch, Jonathan
Comparing mind perception in strategic exchanges: human-agent negotiation, dictator and ultimatum games Journal Article
In: J Multimodal User Interfaces, 2021, ISSN: 1783-7677, 1783-8738.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{lee_comparing_2021,
title = {Comparing mind perception in strategic exchanges: human-agent negotiation, dictator and ultimatum games},
author = {Minha Lee and Gale Lucas and Jonathan Gratch},
url = {http://link.springer.com/10.1007/s12193-020-00356-6},
doi = {10.1007/s12193-020-00356-6},
issn = {1783-7677, 1783-8738},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
journal = {J Multimodal User Interfaces},
abstract = {Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In a comparative manner, we observed how perceived minds of agents shape people’s behavior in the dictator game, ultimatum game, and negotiation against artificial agents. To do so, we varied agents’ minds on two dimensions of the mind perception theory: agency (cognitive aptitude) and patiency (affective aptitude) via descriptions and dialogs. In our first study, agents with emotional capacity garnered more allocations in the dictator game, but in the ultimatum game, agents’ described agency and affective capacity, both led to greater offers. In the second study on negotiation, agents ascribed with low-agency traits earned more points than those with high-agency traits, though the negotiation tactic was the same for all agents. Although patiency did not impact game points, participants sent more happy and surprise emojis and emotionally valenced messages to agents that demonstrated emotional capacity during negotiations. Further, our exploratory analyses indicate that people related only to agents with perceived affective aptitude across all games. Both perceived agency and affective capacity contributed to moral standing after dictator and ultimatum games. But after negotiations, only agents with perceived affective capacity were granted moral standing. Manipulating mind dimensions of machines has differing effects on how people react to them in dictator and ultimatum games, compared to a more complex economic exchange like negotiation. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations, in contrast with simple economic games.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In a comparative manner, we observed how perceived minds of agents shape people’s behavior in the dictator game, ultimatum game, and negotiation against artificial agents. To do so, we varied agents’ minds on two dimensions of the mind perception theory: agency (cognitive aptitude) and patiency (affective aptitude) via descriptions and dialogs. In our first study, agents with emotional capacity garnered more allocations in the dictator game, but in the ultimatum game, agents’ described agency and affective capacity, both led to greater offers. In the second study on negotiation, agents ascribed with low-agency traits earned more points than those with high-agency traits, though the negotiation tactic was the same for all agents. Although patiency did not impact game points, participants sent more happy and surprise emojis and emotionally valenced messages to agents that demonstrated emotional capacity during negotiations. Further, our exploratory analyses indicate that people related only to agents with perceived affective aptitude across all games. Both perceived agency and affective capacity contributed to moral standing after dictator and ultimatum games. But after negotiations, only agents with perceived affective capacity were granted moral standing. Manipulating mind dimensions of machines has differing effects on how people react to them in dictator and ultimatum games, compared to a more complex economic exchange like negotiation. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations, in contrast with simple economic games.
Gratch, Jonathan
The Promise and Peril of Automated Negotiators Journal Article
In: Negotiation Journal, vol. 37, no. 1, pp. 13–34, 2021, ISSN: 0748-4526, 1571-9979.
Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@article{gratch_promise_2021,
title = {The Promise and Peril of Automated Negotiators},
author = {Jonathan Gratch},
url = {https://onlinelibrary.wiley.com/doi/10.1111/nejo.12348},
doi = {10.1111/nejo.12348},
issn = {0748-4526, 1571-9979},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-14},
journal = {Negotiation Journal},
volume = {37},
number = {1},
pages = {13–34},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Bell, Benjamin; Bennett, Winston Wink; Nye, Benjamin; Kelsey, Elaine
Helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations Proceedings Article
In: Sottilare, Robert A.; Schwarz, Jessica (Ed.): Adaptive Instructional Systems. Adaptation Strategies and Methods, pp. 3–14, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-77873-6.
Abstract | Links | BibTeX | Tags: Machine Learning, Virtual Humans
@inproceedings{bell_helping_2021,
title = {Helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations},
author = {Benjamin Bell and Winston Wink Bennett and Benjamin Nye and Elaine Kelsey},
editor = {Robert A. Sottilare and Jessica Schwarz},
url = {https://link.springer.com/chapter/10.1007/978-3-030-77873-6_1},
doi = {10.1007/978-3-030-77873-6_1},
isbn = {978-3-030-77873-6},
year = {2021},
date = {2021-01-01},
booktitle = {Adaptive Instructional Systems. Adaptation Strategies and Methods},
pages = {3–14},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Adapting training in real time can be challenging for instructors. Real-time simulation can present rapid sequences of events, making it difficult for an instructor to attribute errors or omissions to specific underling gaps in skills and knowledge. Monitoring multiple students simultaneously imposes additional attentional workload on an instructor. This challenge can be further exacerbated when an instructor’s view of the student is obscured by virtual reality (VR) equipment. To support instructors’ ability to adapt training, Eduworks and USC’s Institute for Creative Technologies are developing machine learning (ML) models that can measure user engagement during training simulations and offer recommendations for restoring lapses in engagement. We have created a system, called the Observational Motivation and Engagement Generalized Appliance (OMEGA), which we tested in the context of a new U.S. Air Force approach to Specialized Undergraduate Pilot Training (SUPT) called Pilot Training Next (PTN). PTN integrates traditional flying sorties with VR-enabled ground-based training devices to achieve training efficiencies, improve readiness, and increase throughput. The virtual environment provides a rich source of raw data that machine learning models can use to associate user activity with user engagement. We created a testbed for data capture to construct the ML models, based on theoretical foundations we developed previously. Our research explores OMEGA’s potential to help alert an instructor pilot (IP) to student distraction by flagging attention and engagement lapses. Our hypothesis is that OMEGA could help an IP adapt learning, and potentially manage multiple students at the same time, with alerts of lapsed attention and recommendations for restoring engagement. To test this hypothesis, we ran pilots through multiple PTN scenarios to create data for training the model. In this paper, we report on work to create machine learning models using three different techniques, and present model performance data using standard machine learning metrics. We discuss the modeling approach used to generate instructor recommendations. Future work will present results from a formative evaluation using instructor pilots. These early findings provide preliminary validation for the use of ML models for learning to detect engagement from the rich data sources characteristic of virtual environments. These findings will be applicable across a broad range of conventional and VR training applications.},
keywords = {Machine Learning, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Adapting training in real time can be challenging for instructors. Real-time simulation can present rapid sequences of events, making it difficult for an instructor to attribute errors or omissions to specific underling gaps in skills and knowledge. Monitoring multiple students simultaneously imposes additional attentional workload on an instructor. This challenge can be further exacerbated when an instructor’s view of the student is obscured by virtual reality (VR) equipment. To support instructors’ ability to adapt training, Eduworks and USC’s Institute for Creative Technologies are developing machine learning (ML) models that can measure user engagement during training simulations and offer recommendations for restoring lapses in engagement. We have created a system, called the Observational Motivation and Engagement Generalized Appliance (OMEGA), which we tested in the context of a new U.S. Air Force approach to Specialized Undergraduate Pilot Training (SUPT) called Pilot Training Next (PTN). PTN integrates traditional flying sorties with VR-enabled ground-based training devices to achieve training efficiencies, improve readiness, and increase throughput. The virtual environment provides a rich source of raw data that machine learning models can use to associate user activity with user engagement. We created a testbed for data capture to construct the ML models, based on theoretical foundations we developed previously. Our research explores OMEGA’s potential to help alert an instructor pilot (IP) to student distraction by flagging attention and engagement lapses. Our hypothesis is that OMEGA could help an IP adapt learning, and potentially manage multiple students at the same time, with alerts of lapsed attention and recommendations for restoring engagement. To test this hypothesis, we ran pilots through multiple PTN scenarios to create data for training the model. In this paper, we report on work to create machine learning models using three different techniques, and present model performance data using standard machine learning metrics. We discuss the modeling approach used to generate instructor recommendations. Future work will present results from a formative evaluation using instructor pilots. These early findings provide preliminary validation for the use of ML models for learning to detect engagement from the rich data sources characteristic of virtual environments. These findings will be applicable across a broad range of conventional and VR training applications.
2020
Brixey, Jacqueline; Traum, David
Masheli: A Choctaw-English bilingual chatbot Book Section
In: Conversational Dialogue Systems for the Next Decade, pp. 41–50, Springer, Switzerland, 2020.
Abstract | Links | BibTeX | Tags: ARO-Coop, Natural Language, UARC, Virtual Humans
@incollection{brixey_masheli_2020,
title = {Masheli: A Choctaw-English bilingual chatbot},
author = {Jacqueline Brixey and David Traum},
url = {https://link.springer.com/chapter/10.1007/978-981-15-8395-7_4},
year = {2020},
date = {2020-10-01},
booktitle = {Conversational Dialogue Systems for the Next Decade},
pages = {41–50},
publisher = {Springer},
address = {Switzerland},
abstract = {We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to pratice conversational skills. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.},
keywords = {ARO-Coop, Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to pratice conversational skills. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.
Mell, Johnathan; Lucas, Gale M.; Gratch, Jonathan
Varied Magnitude Favor Exchange in Human-Agent Negotiation Proceedings Article
In: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, pp. 1–8, ACM, Virtual Event Scotland UK, 2020, ISBN: 978-1-4503-7586-3.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{mell_varied_2020,
title = {Varied Magnitude Favor Exchange in Human-Agent Negotiation},
author = {Johnathan Mell and Gale M. Lucas and Jonathan Gratch},
url = {https://dl.acm.org/doi/10.1145/3383652.3423866},
doi = {10.1145/3383652.3423866},
isbn = {978-1-4503-7586-3},
year = {2020},
date = {2020-10-01},
booktitle = {Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents},
pages = {1–8},
publisher = {ACM},
address = {Virtual Event Scotland UK},
abstract = {Agents that interact with humans in complex, social tasks need the ability to comprehend as well as employ common social strategies. In negotiation, there is ample evidence of such techniques being used efficaciously in human interchanges. In this work, we demonstrate a new design for socially-aware agents that employ one such technique—favor exchange—in order to gain value when playing against humans. In an online study of a robust, simulated social negotiation task, we show that these agents are effective against real human participants. In particular, we show that agents that ask for favors during the course of a repeated set of negotiations are more successful than those that do not. Additionally, previous work has demonstrated that humans can detect when agents betray them by failing to return favors that were previously promised. By contrast, this work indicates that these betrayal techniques may go largely undetected in complex scenarios.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Agents that interact with humans in complex, social tasks need the ability to comprehend as well as employ common social strategies. In negotiation, there is ample evidence of such techniques being used efficaciously in human interchanges. In this work, we demonstrate a new design for socially-aware agents that employ one such technique—favor exchange—in order to gain value when playing against humans. In an online study of a robust, simulated social negotiation task, we show that these agents are effective against real human participants. In particular, we show that agents that ask for favors during the course of a repeated set of negotiations are more successful than those that do not. Additionally, previous work has demonstrated that humans can detect when agents betray them by failing to return favors that were previously promised. By contrast, this work indicates that these betrayal techniques may go largely undetected in complex scenarios.
Hartholt, Arno; Reilly, Adam; Fast, Ed; Mozgai, Sharon
Introducing Canvas: Combining Nonverbal Behavior Generation with User-Generated Content to Rapidly Create Educational Videos Proceedings Article
In: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, pp. 1–3, ACM, Virtual Event Scotland UK, 2020, ISBN: 978-1-4503-7586-3.
Abstract | Links | BibTeX | Tags: VHTL, Virtual Humans
@inproceedings{hartholt_introducing_2020,
title = {Introducing Canvas: Combining Nonverbal Behavior Generation with User-Generated Content to Rapidly Create Educational Videos},
author = {Arno Hartholt and Adam Reilly and Ed Fast and Sharon Mozgai},
url = {https://dl.acm.org/doi/10.1145/3383652.3423880},
doi = {10.1145/3383652.3423880},
isbn = {978-1-4503-7586-3},
year = {2020},
date = {2020-10-01},
booktitle = {Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents},
pages = {1–3},
publisher = {ACM},
address = {Virtual Event Scotland UK},
abstract = {Rapidly creating educational content that is effective, engaging, and low-cost is a challenge. We present Canvas, a tool for educators that addresses this challenge by enabling the generation of educational video, led by an intelligent virtual agent, that combines rapid nonverbal behavior generation techniques with end-user facing authoring tools. With Canvas, educators can easily produce compelling educational videos with a minimum of investment by leveraging existing content provided by the tool (e.g., characters and environments) while incorporating their own custom content (e.g., images and video clips). Canvas has been delivered to the Smithsonian Science Education Center and is currently being evaluated internally before wider release. We discuss the system, feature set, design process, and lessons learned.},
keywords = {VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Rapidly creating educational content that is effective, engaging, and low-cost is a challenge. We present Canvas, a tool for educators that addresses this challenge by enabling the generation of educational video, led by an intelligent virtual agent, that combines rapid nonverbal behavior generation techniques with end-user facing authoring tools. With Canvas, educators can easily produce compelling educational videos with a minimum of investment by leveraging existing content provided by the tool (e.g., characters and environments) while incorporating their own custom content (e.g., images and video clips). Canvas has been delivered to the Smithsonian Science Education Center and is currently being evaluated internally before wider release. We discuss the system, feature set, design process, and lessons learned.
Wang, Ning; Pacheco, Luz; Merchant, Chirag; Skistad, Kristian; Jethwani, Aayushi
The Design of Charismatic Behaviors for Virtual Humans Proceedings Article
In: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, pp. 1–8, Association for Computing Machinery, New York, NY, USA, 2020, ISBN: 978-1-4503-7586-3.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{wang_design_2020,
title = {The Design of Charismatic Behaviors for Virtual Humans},
author = {Ning Wang and Luz Pacheco and Chirag Merchant and Kristian Skistad and Aayushi Jethwani},
url = {https://doi.org/10.1145/3383652.3423867},
doi = {10.1145/3383652.3423867},
isbn = {978-1-4503-7586-3},
year = {2020},
date = {2020-10-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents},
pages = {1–8},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {IVA '20},
abstract = {Charisma is a powerful device of communication and persuasion. Researchers have pinpointed specific behaviors that contribute to the perception of charisma. How can we realize such behaviors in a virtual character? In this paper, we discuss our work in the design of charismatic behavior for a virtual human. We developed a series of verbal and nonverbal (with the focus on voice) charismatic strategies based on the analysis of behaviors of charismatic leaders. We developed scripted speech dialogues with the verbal strategies and recorded the speeches with actors using the nonverbal strategies. The dialogue is further implemented in a virtual human, embedded in a virtual classroom, to give a lecture on the human circulatory system. We conducted a study with the virtual human to assess the impact of charismatic verbal and nonverbal behaviors on perceived charisma. The results show the positive impact of the use of verbal strategies and how the use of voice can influence such impact. The results shed light on the next steps needed to automatically generate charismatic speech, voices, and gestures for virtual characters.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Charisma is a powerful device of communication and persuasion. Researchers have pinpointed specific behaviors that contribute to the perception of charisma. How can we realize such behaviors in a virtual character? In this paper, we discuss our work in the design of charismatic behavior for a virtual human. We developed a series of verbal and nonverbal (with the focus on voice) charismatic strategies based on the analysis of behaviors of charismatic leaders. We developed scripted speech dialogues with the verbal strategies and recorded the speeches with actors using the nonverbal strategies. The dialogue is further implemented in a virtual human, embedded in a virtual classroom, to give a lecture on the human circulatory system. We conducted a study with the virtual human to assess the impact of charismatic verbal and nonverbal behaviors on perceived charisma. The results show the positive impact of the use of verbal strategies and how the use of voice can influence such impact. The results shed light on the next steps needed to automatically generate charismatic speech, voices, and gestures for virtual characters.
Gordon, Carla; Georgila, Kallirroi; Yanov, Volodymyr; Traum, David
Towards Personalization of Spoken Dialogue System Communication Strategies Book Section
In: Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 145–160, Springer Singapore, Singapore, 2020, ISBN: 978-981-15-8394-0 978-981-15-8395-7.
Abstract | Links | BibTeX | Tags: ARO-Coop, Dialogue, Natural Language, UARC, Virtual Humans
@incollection{gordon_towards_2020,
title = {Towards Personalization of Spoken Dialogue System Communication Strategies},
author = {Carla Gordon and Kallirroi Georgila and Volodymyr Yanov and David Traum},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
isbn = {978-981-15-8394-0 978-981-15-8395-7},
year = {2020},
date = {2020-09-01},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {145–160},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
keywords = {ARO-Coop, Dialogue, Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
Zhu, Runhe; Lucas, Gale M.; Becerik-Gerber, Burcin; Southers, Erroll G.
Building preparedness in response to active shooter incidents: Results of focus group interviews Journal Article
In: International Journal of Disaster Risk Reduction, vol. 48, pp. 101617, 2020, ISSN: 22124209.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@article{zhu_building_2020,
title = {Building preparedness in response to active shooter incidents: Results of focus group interviews},
author = {Runhe Zhu and Gale M. Lucas and Burcin Becerik-Gerber and Erroll G. Southers},
url = {https://linkinghub.elsevier.com/retrieve/pii/S221242091931427X},
doi = {10.1016/j.ijdrr.2020.101617},
issn = {22124209},
year = {2020},
date = {2020-09-01},
journal = {International Journal of Disaster Risk Reduction},
volume = {48},
pages = {101617},
abstract = {Active shooter incidents present an increasing threat to the American society. Many of these incidents occur in building environments, therefore, it is important to consider design and security elements in buildings to decrease the risk of active shooter incidents. This study aims to assess current security countermeasures and identify varying considerations associated with implementing these countermeasures. Fifteen participants, with expertise and experience in a diverse collection of operational and organizational backgrounds, including se curity, engineering, law enforcement, emergency management and policy making, participated in three focus group interviews. The participants identified a list of countermeasures that have been used for active shooter incidents. Important determinants for the effectiveness of countermeasures include their influence on occupants’ behavior during active shooter incidents, and occupants’ and administrators’ awareness of how to use them effectively. The nature of incidents (e.g., internal vs. external threats), building type (e.g., office buildings vs. school buildings), and occupants (e.g., students of different ages) were also recognized to affect the selection of appropriate countermeasures. The nexus between emergency preparedness and normal operations, and the importance of tradeoffs such as the ones between cost, aesthetics, maintenance needs and the influence on oc cupants’ daily activities were also discussed. To ensure the effectiveness of countermeasures and improve safety, the participants highlighted the importance of both training and practice, for occupants and administrators (e.g., first responder teams). The interview results suggested that further study of the relationship between security countermeasures and occupants’ and administrators’ responses, as well as efficient training approaches are needed.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Active shooter incidents present an increasing threat to the American society. Many of these incidents occur in building environments, therefore, it is important to consider design and security elements in buildings to decrease the risk of active shooter incidents. This study aims to assess current security countermeasures and identify varying considerations associated with implementing these countermeasures. Fifteen participants, with expertise and experience in a diverse collection of operational and organizational backgrounds, including se curity, engineering, law enforcement, emergency management and policy making, participated in three focus group interviews. The participants identified a list of countermeasures that have been used for active shooter incidents. Important determinants for the effectiveness of countermeasures include their influence on occupants’ behavior during active shooter incidents, and occupants’ and administrators’ awareness of how to use them effectively. The nature of incidents (e.g., internal vs. external threats), building type (e.g., office buildings vs. school buildings), and occupants (e.g., students of different ages) were also recognized to affect the selection of appropriate countermeasures. The nexus between emergency preparedness and normal operations, and the importance of tradeoffs such as the ones between cost, aesthetics, maintenance needs and the influence on oc cupants’ daily activities were also discussed. To ensure the effectiveness of countermeasures and improve safety, the participants highlighted the importance of both training and practice, for occupants and administrators (e.g., first responder teams). The interview results suggested that further study of the relationship between security countermeasures and occupants’ and administrators’ responses, as well as efficient training approaches are needed.
Hartholt, Arno; Fast, Ed; Reilly, Adam; Whitcup, Wendy; Liewer, Matt; Mozgai, Sharon
Multi-Platform Expansion of the Virtual Human Toolkit: Ubiquitous Conversational Agents Journal Article
In: International Journal of Semantic Computing, vol. 14, no. 03, pp. 315–332, 2020, ISSN: 1793-351X, 1793-7108.
Abstract | Links | BibTeX | Tags: UARC, VHTL, Virtual Humans
@article{hartholt_multi-platform_2020,
title = {Multi-Platform Expansion of the Virtual Human Toolkit: Ubiquitous Conversational Agents},
author = {Arno Hartholt and Ed Fast and Adam Reilly and Wendy Whitcup and Matt Liewer and Sharon Mozgai},
url = {https://www.worldscientific.com/doi/abs/10.1142/S1793351X20400127},
doi = {10.1142/S1793351X20400127},
issn = {1793-351X, 1793-7108},
year = {2020},
date = {2020-09-01},
journal = {International Journal of Semantic Computing},
volume = {14},
number = {03},
pages = {315–332},
abstract = {We present an extension of the Virtual Human Toolkit to include a range of computing platforms, including mobile, web, Virtual Reality (VR) and Augmented Reality (AR). The Toolkit uses a mix of in-house and commodity technologies to support audio-visual sensing, speech recognition, natural language processing, nonverbal behavior generation and realization, text-to-speech generation and rendering. It has been extended to support computing platforms beyond Windows by leveraging microservices. The resulting framework maintains the modularity of the underlying architecture, allows re-use of both logic and content through cloud services, and is extensible by porting lightweight clients. We present the current state of the framework, discuss how we model and animate our characters, and offer lessons learned through several use cases, including expressive character animation in seated VR, shared space and navigation in room-scale VR, autonomous AI in mobile AR, and real-time user performance feedback leveraging mobile sensors in headset AR.},
keywords = {UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
We present an extension of the Virtual Human Toolkit to include a range of computing platforms, including mobile, web, Virtual Reality (VR) and Augmented Reality (AR). The Toolkit uses a mix of in-house and commodity technologies to support audio-visual sensing, speech recognition, natural language processing, nonverbal behavior generation and realization, text-to-speech generation and rendering. It has been extended to support computing platforms beyond Windows by leveraging microservices. The resulting framework maintains the modularity of the underlying architecture, allows re-use of both logic and content through cloud services, and is extensible by porting lightweight clients. We present the current state of the framework, discuss how we model and animate our characters, and offer lessons learned through several use cases, including expressive character animation in seated VR, shared space and navigation in room-scale VR, autonomous AI in mobile AR, and real-time user performance feedback leveraging mobile sensors in headset AR.
Mell, Johnathan; Lucas, Gale; Mozgai, Sharon; Gratch, Jonathan
The Effects of Experience on Deception in Human-Agent Negotiation Journal Article
In: Journal of Artificial Intelligence Research, vol. 68, pp. 633–660, 2020, ISSN: 1076-9757.
Abstract | Links | BibTeX | Tags: UARC, VHTL, Virtual Humans
@article{mell_effects_2020,
title = {The Effects of Experience on Deception in Human-Agent Negotiation},
author = {Johnathan Mell and Gale Lucas and Sharon Mozgai and Jonathan Gratch},
url = {https://www.jair.org/index.php/jair/article/view/11924},
doi = {10.1613/jair.1.11924},
issn = {1076-9757},
year = {2020},
date = {2020-08-01},
urldate = {2023-03-31},
journal = {Journal of Artificial Intelligence Research},
volume = {68},
pages = {633–660},
abstract = {Negotiation is the complex social process by which multiple parties come to mutual agreement over a series of issues. As such, it has proven to be a key challenge problem for designing adequately social AIs that can effectively navigate this space. Artificial AI agents that are capable of negotiating must be capable of realizing policies and strategies that govern offer acceptances, offer generation, preference elicitation, and more. But the next generation of agents must also adapt to reflect their users’ experiences.
The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.
This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.},
keywords = {UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Negotiation is the complex social process by which multiple parties come to mutual agreement over a series of issues. As such, it has proven to be a key challenge problem for designing adequately social AIs that can effectively navigate this space. Artificial AI agents that are capable of negotiating must be capable of realizing policies and strategies that govern offer acceptances, offer generation, preference elicitation, and more. But the next generation of agents must also adapt to reflect their users’ experiences.
The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.
This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.
The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.
This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.
Brixey, Jacqueline; Artstein, Ron
ChoCo: a multimodal corpus of the Choctaw language Journal Article
In: Language Resources and Evaluation, 2020, ISSN: 1574-020X, 1574-0218.
Abstract | Links | BibTeX | Tags: ARO-Coop, UARC, Virtual Humans
@article{brixey_choco_2020,
title = {ChoCo: a multimodal corpus of the Choctaw language},
author = {Jacqueline Brixey and Ron Artstein},
url = {http://link.springer.com/10.1007/s10579-020-09494-5},
doi = {10.1007/s10579-020-09494-5},
issn = {1574-020X, 1574-0218},
year = {2020},
date = {2020-07-01},
journal = {Language Resources and Evaluation},
abstract = {This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.},
keywords = {ARO-Coop, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.
Czyzewski, Adam; Dalton, Jeffrey; Leuski, Anton
Agent Dialogue: A Platform for Conversational Information Seeking Experimentation Proceedings Article
In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2121–2124, ACM, Virtual Event China, 2020, ISBN: 978-1-4503-8016-4.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{czyzewski_agent_2020,
title = {Agent Dialogue: A Platform for Conversational Information Seeking Experimentation},
author = {Adam Czyzewski and Jeffrey Dalton and Anton Leuski},
url = {https://dl.acm.org/doi/10.1145/3397271.3401397},
doi = {10.1145/3397271.3401397},
isbn = {978-1-4503-8016-4},
year = {2020},
date = {2020-07-01},
booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2121–2124},
publisher = {ACM},
address = {Virtual Event China},
abstract = {Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade stateof-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade stateof-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.
Nye, Benjamin D.; Davis, Dan M.; Rizvi, Sanad Z.; Carr, Kayla; Swartout, William; Thacker, Raj; Shaw, Kenneth
Feasibility and usability of MentorPal, a framework for rapid development of virtual mentors Journal Article
In: Journal of Research on Technology in Education, pp. 1–23, 2020, ISSN: 1539-1523, 1945-0818.
Abstract | Links | BibTeX | Tags: Learning Sciences, Virtual Humans
@article{nye_feasibility_2020,
title = {Feasibility and usability of MentorPal, a framework for rapid development of virtual mentors},
author = {Benjamin D. Nye and Dan M. Davis and Sanad Z. Rizvi and Kayla Carr and William Swartout and Raj Thacker and Kenneth Shaw},
url = {https://www.tandfonline.com/doi/full/10.1080/15391523.2020.1771640},
doi = {10.1080/15391523.2020.1771640},
issn = {1539-1523, 1945-0818},
year = {2020},
date = {2020-07-01},
journal = {Journal of Research on Technology in Education},
pages = {1–23},
abstract = {One-on-one mentoring is an effective method to help novices with career development. However, traditional mentoring scales poorly. To address this problem, MentorPal emulates conversations with a panel of virtual mentors based on recordings of real STEM professionals. Students freely ask questions as they might in a career fair, while machine learning algorithms attempt to provide the best answers. MentorPal has developed strategies for the rapid development of new virtual mentors, where training data will be sparse. In a usability study, 31 high school students self-reported a) increased career knowledge and confidence, b) positive ease-of-use, and that c) mentors were helpful (87%) but often did not cover their preferred career (29%). Results demonstrate the feasibility of scalable virtual mentoring, but efficacy studies are needed to evaluate the impact of virtual mentors, particularly for groups with limited STEM opportunities.},
keywords = {Learning Sciences, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
One-on-one mentoring is an effective method to help novices with career development. However, traditional mentoring scales poorly. To address this problem, MentorPal emulates conversations with a panel of virtual mentors based on recordings of real STEM professionals. Students freely ask questions as they might in a career fair, while machine learning algorithms attempt to provide the best answers. MentorPal has developed strategies for the rapid development of new virtual mentors, where training data will be sparse. In a usability study, 31 high school students self-reported a) increased career knowledge and confidence, b) positive ease-of-use, and that c) mentors were helpful (87%) but often did not cover their preferred career (29%). Results demonstrate the feasibility of scalable virtual mentoring, but efficacy studies are needed to evaluate the impact of virtual mentors, particularly for groups with limited STEM opportunities.
Mozgai, Sharon; Hartholt, Arno; Akinyemi, Dayo; Kubicek, Katarina; Rizzo, Albert (Skip); Kipke, Michele
In: HCI International 2020 - Posters, vol. 1225, pp. 304–307, Springer International Publishing, Cham, Switzerland, 2020, ISBN: 978-3-030-50728-2 978-3-030-50729-9.
Abstract | Links | BibTeX | Tags: MedVR, VHTL, Virtual Humans
@incollection{mozgai_development_2020,
title = {Development and Initial Feasibility Testing of the Virtual Research Navigator (VRN): A Public-Facing Agent-Based Educational System for Clinical Research Participation},
author = {Sharon Mozgai and Arno Hartholt and Dayo Akinyemi and Katarina Kubicek and Albert (Skip) Rizzo and Michele Kipke},
url = {http://link.springer.com/10.1007/978-3-030-50729-9_43},
doi = {10.1007/978-3-030-50729-9_43},
isbn = {978-3-030-50728-2 978-3-030-50729-9},
year = {2020},
date = {2020-07-01},
booktitle = {HCI International 2020 - Posters},
volume = {1225},
pages = {304–307},
publisher = {Springer International Publishing},
address = {Cham, Switzerland},
abstract = {The overall goal of VRN is to develop a novel technology solution at Children’s Hospital Los Angeles (CHLA) to overcome barriers that prevent the recruitment of diverse patient populations to clinical trials by providing both caregivers and children with an interactive educational experience. This system consists of 1) an intelligent agent called Zippy that users interact with by keyboard or voice input, 2) a series of videos covering topics including Privacy, Consent and Benefits, and 3) a UI that guides users through all available content. Pre- and post-questionnaires assessed willingness to participate in clinical research and found participants either increased or maintained their level of willingness to participate in research studies. Additionally, qualitative analysis of interview data revealed participants rated the overall interaction favorably and believed Zippy to be more fun, less judgmental and less threatening than interacting with a human. Future iterations are in-progress based on the user-feedback},
keywords = {MedVR, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
The overall goal of VRN is to develop a novel technology solution at Children’s Hospital Los Angeles (CHLA) to overcome barriers that prevent the recruitment of diverse patient populations to clinical trials by providing both caregivers and children with an interactive educational experience. This system consists of 1) an intelligent agent called Zippy that users interact with by keyboard or voice input, 2) a series of videos covering topics including Privacy, Consent and Benefits, and 3) a UI that guides users through all available content. Pre- and post-questionnaires assessed willingness to participate in clinical research and found participants either increased or maintained their level of willingness to participate in research studies. Additionally, qualitative analysis of interview data revealed participants rated the overall interaction favorably and believed Zippy to be more fun, less judgmental and less threatening than interacting with a human. Future iterations are in-progress based on the user-feedback
Rayatdoost, Soheil; Rudrauf, David; Soleymani, Mohammad
Expression-Guided EEG Representation Learning for Emotion Recognition Proceedings Article
In: Proceedings of the ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3222–3226, IEEE, Barcelona, Spain, 2020, ISBN: 978-1-5090-6631-5.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{rayatdoost_expression-guided_2020,
title = {Expression-Guided EEG Representation Learning for Emotion Recognition},
author = {Soheil Rayatdoost and David Rudrauf and Mohammad Soleymani},
url = {https://ieeexplore.ieee.org/document/9053004/},
doi = {10.1109/ICASSP40776.2020.9053004},
isbn = {978-1-5090-6631-5},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {3222–3226},
publisher = {IEEE},
address = {Barcelona, Spain},
abstract = {Learning a joint and coordinated representation between different modalities can improve multimodal emotion recognition. In this paper, we propose a deep representation learning approach for emotion recognition from electroencephalogram (EEG) signals guided by facial electromyogram (EMG) and electrooculogram (EOG) signals. We recorded EEG, EMG and EOG signals from 60 participants who watched 40 short videos and self-reported their emotions. A cross-modal encoder that jointly learns the features extracted from facial and ocular expressions and EEG responses was designed and evaluated on our recorded data and MAHOB-HCI, a publicly available database. We demonstrate that the proposed representation is able to improve emotion recognition performance. We also show that the learned representation can be transferred to a different database without EMG and EOG and achieve superior performance. Methods that fuse behavioral and neural responses can be deployed in wearable emotion recognition solutions, practical in situations in which computer vision expression recognition is not feasible.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Learning a joint and coordinated representation between different modalities can improve multimodal emotion recognition. In this paper, we propose a deep representation learning approach for emotion recognition from electroencephalogram (EEG) signals guided by facial electromyogram (EMG) and electrooculogram (EOG) signals. We recorded EEG, EMG and EOG signals from 60 participants who watched 40 short videos and self-reported their emotions. A cross-modal encoder that jointly learns the features extracted from facial and ocular expressions and EEG responses was designed and evaluated on our recorded data and MAHOB-HCI, a publicly available database. We demonstrate that the proposed representation is able to improve emotion recognition performance. We also show that the learned representation can be transferred to a different database without EMG and EOG and achieve superior performance. Methods that fuse behavioral and neural responses can be deployed in wearable emotion recognition solutions, practical in situations in which computer vision expression recognition is not feasible.
Lei, Su; Stefanov, Kalin; Gratch, Jonathan
Emotion or expressivity? An automated analysis of nonverbal perception in a social dilemma Proceedings Article
In: Proceedings of the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) (FG), pp. 8, IEEE, Buenos Aires, Argentina, 2020.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{lei_emotion_2020,
title = {Emotion or expressivity? An automated analysis of nonverbal perception in a social dilemma},
author = {Su Lei and Kalin Stefanov and Jonathan Gratch},
url = {https://www.computer.org/csdl/proceedings-article/fg/2020/307900a770/1kecIWT5wmA},
doi = {10.1109/FG47880.2020.00123},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) (FG)},
pages = {8},
publisher = {IEEE},
address = {Buenos Aires, Argentina},
abstract = {An extensive body of research has examined how specific emotional expressions shape social perceptions and social decisions, yet recent scholarship in emotion research has raised questions about the validity of emotion as a construct. In this article, we contrast the value of measuring emotional expressions with the more general construct of expressivity (in the sense of conveying a thought or emotion through any nonverbal behavior) and develop models that can automatically extract perceived expressivity from videos. Although less extensive, a solid body of research has shown expressivity to be an important element when studying interpersonal perception, particularly in psychiatric contexts. Here we examine the role expressivity plays in predicting social perceptions and decisions in the context of a social dilemma. We show that perceivers use more than facial expressions when making judgments of expressivity and see these expressions as conveying thoughts as well as emotions (although facial expressions and emotional attributions explain most of the variance in these judgments). We next show that expressivity can be predicted with high accuracy using Lasso and random forests. Our analysis shows that features related to motion dynamics are particularly important for modeling these judgments. We also show that learned models of expressivity have value in recognizing important aspects of a social situation. First, we revisit a previously published finding which showed that smile intensity was associated with the unexpectedness of outcomes in social dilemmas; instead, we show that expressivity is a better predictor (and explanation) of this finding. Second, we provide preliminary evidence that expressivity is useful for identifying “moments of interest” in a video sequence.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
An extensive body of research has examined how specific emotional expressions shape social perceptions and social decisions, yet recent scholarship in emotion research has raised questions about the validity of emotion as a construct. In this article, we contrast the value of measuring emotional expressions with the more general construct of expressivity (in the sense of conveying a thought or emotion through any nonverbal behavior) and develop models that can automatically extract perceived expressivity from videos. Although less extensive, a solid body of research has shown expressivity to be an important element when studying interpersonal perception, particularly in psychiatric contexts. Here we examine the role expressivity plays in predicting social perceptions and decisions in the context of a social dilemma. We show that perceivers use more than facial expressions when making judgments of expressivity and see these expressions as conveying thoughts as well as emotions (although facial expressions and emotional attributions explain most of the variance in these judgments). We next show that expressivity can be predicted with high accuracy using Lasso and random forests. Our analysis shows that features related to motion dynamics are particularly important for modeling these judgments. We also show that learned models of expressivity have value in recognizing important aspects of a social situation. First, we revisit a previously published finding which showed that smile intensity was associated with the unexpectedness of outcomes in social dilemmas; instead, we show that expressivity is a better predictor (and explanation) of this finding. Second, we provide preliminary evidence that expressivity is useful for identifying “moments of interest” in a video sequence.
Alavi, Seyed Hossein; Leuski, Anton; Traum, David
Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? Proceedings Article
In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 735–742, European Language Resources Association, Marseille, France, 2020.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{alavi_which_2020,
title = {Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?},
author = {Seyed Hossein Alavi and Anton Leuski and David Traum},
url = {https://www.aclweb.org/anthology/2020.lrec-1.92/},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
pages = {735–742},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).
Bonial, Claire; Donatelli, Lucia; Abrams, Mitchell; Lukin, Stephanie M; Tratz, Stephen; Marge, Matthew; Artstein, Ron; Traum, David; Voss, Clare R
Dialogue-AMR: Abstract Meaning Representation for Dialogue Proceedings Article
In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 12, European Language Resources Association, Marseille, France, 2020.
Abstract | Links | BibTeX | Tags: ARL, ARO-Coop, DoD, UARC, Virtual Humans
@inproceedings{bonial_dialogue-amr_2020,
title = {Dialogue-AMR: Abstract Meaning Representation for Dialogue},
author = {Claire Bonial and Lucia Donatelli and Mitchell Abrams and Stephanie M Lukin and Stephen Tratz and Matthew Marge and Ron Artstein and David Traum and Clare R Voss},
url = {https://www.aclweb.org/anthology/2020.lrec-1.86/},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
pages = {12},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus—a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.},
keywords = {ARL, ARO-Coop, DoD, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus—a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.
Bellas, Alexandria; Perrin, Stefawn; Malone, Brandon; Rogers, Kaytlin; Lucas, Gale; Phillips, Elizabeth; Tossell, Chad; de Visser, Ewart
Rapport Building with Social Robots as a Method for Improving Mission Debriefing in Human-Robot Teams Proceedings Article
In: Proceedings of the 2020 Systems and Information Engineering Design Symposium (SIEDS), pp. 160–163, IEEE, Charlottesville, VA, USA, 2020, ISBN: 978-1-7281-7145-6.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{bellas_rapport_2020,
title = {Rapport Building with Social Robots as a Method for Improving Mission Debriefing in Human-Robot Teams},
author = {Alexandria Bellas and Stefawn Perrin and Brandon Malone and Kaytlin Rogers and Gale Lucas and Elizabeth Phillips and Chad Tossell and Ewart de Visser},
url = {https://ieeexplore.ieee.org/document/9106643/},
doi = {10.1109/SIEDS49339.2020.9106643},
isbn = {978-1-7281-7145-6},
year = {2020},
date = {2020-04-01},
booktitle = {Proceedings of the 2020 Systems and Information Engineering Design Symposium (SIEDS)},
pages = {160–163},
publisher = {IEEE},
address = {Charlottesville, VA, USA},
abstract = {Conflicts may arise at any time during military debriefing meetings, especially in high intensity deployed settings. When such conflicts arise, it takes time to get everyone back into a receptive state of mind so that they engage in reflective discussion rather than unproductive arguing. It has been proposed by some that the use of social robots equipped with social abilities such as emotion regulation through rapport building may help to deescalate these situations to facilitate critical operational decisions. However, in military settings, the same AI agent used in the pre-brief of a mission may not be the same one used in the debrief. The purpose of this study was to determine whether a brief rapport-building session with a social robot could create a connection between a human and a robot agent, and whether consistency in the embodiment of the robot agent was necessary for maintaining this connection once formed. We report the results of a pilot study conducted at the United States Air Force Academy which simulated a military mission (i.e., Gravity and Strike). Participants’ connection with the agent, sense of trust, and overall likeability revealed that early rapport building can be beneficial for military missions.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Conflicts may arise at any time during military debriefing meetings, especially in high intensity deployed settings. When such conflicts arise, it takes time to get everyone back into a receptive state of mind so that they engage in reflective discussion rather than unproductive arguing. It has been proposed by some that the use of social robots equipped with social abilities such as emotion regulation through rapport building may help to deescalate these situations to facilitate critical operational decisions. However, in military settings, the same AI agent used in the pre-brief of a mission may not be the same one used in the debrief. The purpose of this study was to determine whether a brief rapport-building session with a social robot could create a connection between a human and a robot agent, and whether consistency in the embodiment of the robot agent was necessary for maintaining this connection once formed. We report the results of a pilot study conducted at the United States Air Force Academy which simulated a military mission (i.e., Gravity and Strike). Participants’ connection with the agent, sense of trust, and overall likeability revealed that early rapport building can be beneficial for military missions.
Chaffey, Patricia; Artstein, Ron; Georgila, Kallirroi; Pollard, Kimberly A.; Gilani, Setareh Nasihati; Krum, David M.; Nelson, David; Huynh, Kevin; Gainer, Alesia; Alavi, Seyed Hossein; Yahata, Rhys; Leuski, Anton; Yanov, Volodymyr; Traum, David
Human swarm interaction using plays, audibles, and a virtual spokesperson Proceedings Article
In: Proceedings of Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, pp. 40, SPIE, Online Only, United States, 2020, ISBN: 978-1-5106-3603-3 978-1-5106-3604-0.
Abstract | Links | BibTeX | Tags: ARL, DoD, MxR, UARC, Virtual Humans
@inproceedings{chaffey_human_2020,
title = {Human swarm interaction using plays, audibles, and a virtual spokesperson},
author = {Patricia Chaffey and Ron Artstein and Kallirroi Georgila and Kimberly A. Pollard and Setareh Nasihati Gilani and David M. Krum and David Nelson and Kevin Huynh and Alesia Gainer and Seyed Hossein Alavi and Rhys Yahata and Anton Leuski and Volodymyr Yanov and David Traum},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11413/2557573/Human-swarm-interaction-using-plays-audibles-and-a-virtual-spokesperson/10.1117/12.2557573.full},
doi = {10.1117/12.2557573},
isbn = {978-1-5106-3603-3 978-1-5106-3604-0},
year = {2020},
date = {2020-04-01},
booktitle = {Proceedings of Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II},
pages = {40},
publisher = {SPIE},
address = {Online Only, United States},
abstract = {This study explores two hypotheses about human-agent teaming: 1. Real-time coordination among a large set of autonomous robots can be achieved using predefined “plays” which define how to execute a task, and “audibles” which modify the play on the fly; 2. A spokesperson agent can serve as a representative for a group of robots, relaying information between the robots and human teammates. These hypotheses are tested in a simulated game environment: a human participant leads a search-and-rescue operation to evacuate a town threatened by an approaching wildfire, with the object of saving as many lives as possible. The participant communicates verbally with a virtual agent controlling a team of ten aerial robots and one ground vehicle, while observing a live map display with real-time location of the fire and identified survivors. Since full automation is not currently possible, two human controllers control the agent’s speech and actions, and input parameters to the robots, which then operate autonomously until the parameters are changed. Designated plays include monitoring the spread of fire, searching for survivors, broadcasting warnings, guiding residents to safety, and sending the rescue vehicle. A successful evacuation of all the residents requires personal intervention in some cases (e.g., stubborn residents) while delegating other responsibilities to the spokesperson agent and robots, all in a rapidly changing scene. The study records the participants’ verbal and nonverbal behavior in order to identify strategies people use when communicating with robotic swarms, and to collect data for eventual automation.},
keywords = {ARL, DoD, MxR, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
This study explores two hypotheses about human-agent teaming: 1. Real-time coordination among a large set of autonomous robots can be achieved using predefined “plays” which define how to execute a task, and “audibles” which modify the play on the fly; 2. A spokesperson agent can serve as a representative for a group of robots, relaying information between the robots and human teammates. These hypotheses are tested in a simulated game environment: a human participant leads a search-and-rescue operation to evacuate a town threatened by an approaching wildfire, with the object of saving as many lives as possible. The participant communicates verbally with a virtual agent controlling a team of ten aerial robots and one ground vehicle, while observing a live map display with real-time location of the fire and identified survivors. Since full automation is not currently possible, two human controllers control the agent’s speech and actions, and input parameters to the robots, which then operate autonomously until the parameters are changed. Designated plays include monitoring the spread of fire, searching for survivors, broadcasting warnings, guiding residents to safety, and sending the rescue vehicle. A successful evacuation of all the residents requires personal intervention in some cases (e.g., stubborn residents) while delegating other responsibilities to the spokesperson agent and robots, all in a rapidly changing scene. The study records the participants’ verbal and nonverbal behavior in order to identify strategies people use when communicating with robotic swarms, and to collect data for eventual automation.
Mozgai, Sharon; Hartholt, Arno; Leeds, Andrew; Rizzo, Albert ‘Skip’
Iterative Participatory Design for VRET Domain Transfer: From Combat Exposure to Military Sexual Trauma Proceedings Article
In: Proceedings of the 2020 CHI Conference of Human Factors in Computing Systems, pp. 8, ACM, Honolulu, HI, 2020.
Abstract | Links | BibTeX | Tags: MedVR, VHTL, Virtual Humans
@inproceedings{mozgai_iterative_2020,
title = {Iterative Participatory Design for VRET Domain Transfer: From Combat Exposure to Military Sexual Trauma},
author = {Sharon Mozgai and Arno Hartholt and Andrew Leeds and Albert ‘Skip’ Rizzo},
url = {https://dl.acm.org/doi/abs/10.1145/3334480.3375219},
doi = {10.1145/3334480.3375219},
year = {2020},
date = {2020-04-01},
booktitle = {Proceedings of the 2020 CHI Conference of Human Factors in Computing Systems},
pages = {8},
publisher = {ACM},
address = {Honolulu, HI},
abstract = {This case study describes the expansion of the BRAVEMIND virtual reality exposure therapy (VRET) system from the domain of combat-related posttraumatic stress disorder (PTSD) to the domain of PTSD due to Military Sexual Trauma (MST). As VRET continues to demonstrate efficacy in treating PTSD across multiple trauma types and anxiety disorders, adapting existing systems and content to new domains while simultaneously maintaining clinical integrity is becoming a high priority. To develop BRAVEMIND-MST we engaged in an iterative participatory design process with psychologists, engineers, and artists. This first-person account of our collaborative development process focuses on three key areas (1) VR Environment, (2) User-Avatar State, and (3) Events, while detailing the challenges we encountered and lessons learned. This process culminated in eight design guidelines as a first-step in defining a VRET domain transfer methodology.},
keywords = {MedVR, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
This case study describes the expansion of the BRAVEMIND virtual reality exposure therapy (VRET) system from the domain of combat-related posttraumatic stress disorder (PTSD) to the domain of PTSD due to Military Sexual Trauma (MST). As VRET continues to demonstrate efficacy in treating PTSD across multiple trauma types and anxiety disorders, adapting existing systems and content to new domains while simultaneously maintaining clinical integrity is becoming a high priority. To develop BRAVEMIND-MST we engaged in an iterative participatory design process with psychologists, engineers, and artists. This first-person account of our collaborative development process focuses on three key areas (1) VR Environment, (2) User-Avatar State, and (3) Events, while detailing the challenges we encountered and lessons learned. This process culminated in eight design guidelines as a first-step in defining a VRET domain transfer methodology.
Mozgai, Sharon; Hartholt, Arno; Rizzo, Albert
The Passive Sensing Agent: A Multimodal Adaptive mHealth Application Proceedings Article
In: Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 1–3, IEEE, Austin, TX, USA, 2020, ISBN: 978-1-7281-4716-1.
Abstract | Links | BibTeX | Tags: MedVR, UARC, VHTL, Virtual Humans
@inproceedings{mozgai_passive_2020,
title = {The Passive Sensing Agent: A Multimodal Adaptive mHealth Application},
author = {Sharon Mozgai and Arno Hartholt and Albert Rizzo},
url = {https://ieeexplore.ieee.org/document/9156177/},
doi = {10.1109/PerComWorkshops48775.2020.9156177},
isbn = {978-1-7281-4716-1},
year = {2020},
date = {2020-03-01},
booktitle = {Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},
pages = {1–3},
publisher = {IEEE},
address = {Austin, TX, USA},
abstract = {We are demoing the Passive Sensing Agent (PSA), an mHealth virtual human coach, that collects multimodal data through passive sensors native to popular wearables (e.g., Apple Watch, FitBit, and Garmin). This virtual human interface delivers adaptive multi-media content via smartphone application that is specifically tailored to the user in the interdependent domains of physical, cognitive, and emotional health. Initially developed for the military, the PSA delivers health interventions (e.g., educational exercises, physical challenges, and performance feedback) matched to the individual user via novel adaptive logic-based algorithms while employing various behavior change techniques (e.g., goal-setting, barrier identification, rewards, modeling, etc.). A virtual human coach leads all interactions including the firsttime user experience and the brief daily sessions. All interactions were specifically designed to engage and motivate the user while continuously collecting data on their cognitive, emotional, and physical fitness. This multi-component application is integrated and deployed on an iPhone and Apple Watch prototype; a civilian version is currently in-development.},
keywords = {MedVR, UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
We are demoing the Passive Sensing Agent (PSA), an mHealth virtual human coach, that collects multimodal data through passive sensors native to popular wearables (e.g., Apple Watch, FitBit, and Garmin). This virtual human interface delivers adaptive multi-media content via smartphone application that is specifically tailored to the user in the interdependent domains of physical, cognitive, and emotional health. Initially developed for the military, the PSA delivers health interventions (e.g., educational exercises, physical challenges, and performance feedback) matched to the individual user via novel adaptive logic-based algorithms while employing various behavior change techniques (e.g., goal-setting, barrier identification, rewards, modeling, etc.). A virtual human coach leads all interactions including the firsttime user experience and the brief daily sessions. All interactions were specifically designed to engage and motivate the user while continuously collecting data on their cognitive, emotional, and physical fitness. This multi-component application is integrated and deployed on an iPhone and Apple Watch prototype; a civilian version is currently in-development.
Mozgai, Sharon; Hartholt, Arno; Rizzo, Albert "Skip"
An Adaptive Agent-Based Interface for Personalized Health Interventions Proceedings Article
In: Proceedings of the 25th International Conference on Intelligent User Interfaces Companion, pp. 118–119, ACM, Cagliari Italy, 2020, ISBN: 978-1-4503-7513-9.
Abstract | Links | BibTeX | Tags: MedVR, UARC, VHTL, Virtual Humans
@inproceedings{mozgai_adaptive_2020,
title = {An Adaptive Agent-Based Interface for Personalized Health Interventions},
author = {Sharon Mozgai and Arno Hartholt and Albert "Skip" Rizzo},
url = {https://dl.acm.org/doi/10.1145/3379336.3381467},
doi = {10.1145/3379336.3381467},
isbn = {978-1-4503-7513-9},
year = {2020},
date = {2020-03-01},
booktitle = {Proceedings of the 25th International Conference on Intelligent User Interfaces Companion},
pages = {118–119},
publisher = {ACM},
address = {Cagliari Italy},
abstract = {This demo introduces a novel mHealth application with an agentbased interface designed to collect multimodal data with passive sensors native to popular wearables (e.g., Apple Watch, FitBit, and Garmin) as well as through user self-report. This mHealth application delivers personalized and adaptive multimedia content via smartphone application specifically tailored to the user in the interdependent domains of physical, cognitive, and emotional health via novel adaptive logic-based algorithms while employing behavior change techniques (e.g., goal-setting, barrier identification, etc.). A virtual human coach leads all interactions to improve adherence.},
keywords = {MedVR, UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
This demo introduces a novel mHealth application with an agentbased interface designed to collect multimodal data with passive sensors native to popular wearables (e.g., Apple Watch, FitBit, and Garmin) as well as through user self-report. This mHealth application delivers personalized and adaptive multimedia content via smartphone application specifically tailored to the user in the interdependent domains of physical, cognitive, and emotional health via novel adaptive logic-based algorithms while employing behavior change techniques (e.g., goal-setting, barrier identification, etc.). A virtual human coach leads all interactions to improve adherence.
Pilly, Praveen K.; Skorheim, Steven W.; Hubbard, Ryan J.; Ketz, Nicholas A.; Roach, Shane M.; Lerner, Itamar; Jones, Aaron P.; Robert, Bradley; Bryant, Natalie B.; Hartholt, Arno; Mullins, Teagan S.; Choe, Jaehoon; Clark, Vincent P.; Howard, Michael D.
In: Frontiers in Neuroscience, vol. 13, pp. 1416, 2020, ISSN: 1662-453X.
Abstract | Links | BibTeX | Tags: Virtual Humans
@article{pilly_one-shot_2020,
title = {One-Shot Tagging During Wake and Cueing During Sleep With Spatiotemporal Patterns of Transcranial Electrical Stimulation Can Boost Long-Term Metamemory of Individual Episodes in Humans},
author = {Praveen K. Pilly and Steven W. Skorheim and Ryan J. Hubbard and Nicholas A. Ketz and Shane M. Roach and Itamar Lerner and Aaron P. Jones and Bradley Robert and Natalie B. Bryant and Arno Hartholt and Teagan S. Mullins and Jaehoon Choe and Vincent P. Clark and Michael D. Howard},
url = {https://www.frontiersin.org/article/10.3389/fnins.2019.01416/full},
doi = {10.3389/fnins.2019.01416},
issn = {1662-453X},
year = {2020},
date = {2020-01-01},
journal = {Frontiers in Neuroscience},
volume = {13},
pages = {1416},
abstract = {Targeted memory reactivation (TMR) during slow-wave oscillations (SWOs) in sleep has been demonstrated with sensory cues to achieve about 5–12% improvement in post-nap memory performance on simple laboratory tasks. But prior work has not yet addressed the one-shot aspect of episodic memory acquisition, or dealt with the presence of interference from ambient environmental cues in real-world settings. Further, TMR with sensory cues may not be scalable to the multitude of experiences over one’s lifetime. We designed a novel non-invasive non-sensory paradigm that tags one-shot experiences of minute-long naturalistic episodes in immersive virtual reality (VR) with unique spatiotemporal amplitude-modulated patterns (STAMPs) of transcranial electrical stimulation (tES). In particular, we demonstrated that these STAMPs can be reapplied as brief pulses during SWOs in sleep to achieve about 10–20% improvement in the metamemory of targeted episodes compared to the control episodes at 48 hours after initial viewing. We found that STAMPs can not only facilitate but also impair metamemory for the targeted episodes based on an interaction between presleep metamemory and the number of STAMP applications during sleep. Overnight metamemory improvements were mediated by spectral power increases following the offset of STAMPs in the slow-spindle band (8–12 Hz) for left temporal areas in the scalp electroencephalography (EEG) during sleep. These results prescribe an optimal strategy to leverage STAMPs for boosting metamemory and suggest that real-world episodic memories can be modulated in a targeted manner even with coarser, non-invasive spatiotemporal stimulation.},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Targeted memory reactivation (TMR) during slow-wave oscillations (SWOs) in sleep has been demonstrated with sensory cues to achieve about 5–12% improvement in post-nap memory performance on simple laboratory tasks. But prior work has not yet addressed the one-shot aspect of episodic memory acquisition, or dealt with the presence of interference from ambient environmental cues in real-world settings. Further, TMR with sensory cues may not be scalable to the multitude of experiences over one’s lifetime. We designed a novel non-invasive non-sensory paradigm that tags one-shot experiences of minute-long naturalistic episodes in immersive virtual reality (VR) with unique spatiotemporal amplitude-modulated patterns (STAMPs) of transcranial electrical stimulation (tES). In particular, we demonstrated that these STAMPs can be reapplied as brief pulses during SWOs in sleep to achieve about 10–20% improvement in the metamemory of targeted episodes compared to the control episodes at 48 hours after initial viewing. We found that STAMPs can not only facilitate but also impair metamemory for the targeted episodes based on an interaction between presleep metamemory and the number of STAMP applications during sleep. Overnight metamemory improvements were mediated by spectral power increases following the offset of STAMPs in the slow-spindle band (8–12 Hz) for left temporal areas in the scalp electroencephalography (EEG) during sleep. These results prescribe an optimal strategy to leverage STAMPs for boosting metamemory and suggest that real-world episodic memories can be modulated in a targeted manner even with coarser, non-invasive spatiotemporal stimulation.
Gennaro, Mauro; Krumhuber, Eva G.; Lucas, Gale
Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood Journal Article
In: Frontiers in Psychology, vol. 10, pp. 3061, 2020, ISSN: 1664-1078.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@article{de_gennaro_effectiveness_2020,
title = {Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood},
author = {Mauro Gennaro and Eva G. Krumhuber and Gale Lucas},
url = {https://www.frontiersin.org/article/10.3389/fpsyg.2019.03061/full},
doi = {10.3389/fpsyg.2019.03061},
issn = {1664-1078},
year = {2020},
date = {2020-01-01},
journal = {Frontiers in Psychology},
volume = {10},
pages = {3061},
abstract = {From past research it is well known that social exclusion has detrimental consequences for mental health. To deal with these adverse effects, socially excluded individuals frequently turn to other humans for emotional support. While chatbots can elicit social and emotional responses on the part of the human interlocutor, their effectiveness in the context of social exclusion has not been investigated. In the present study, we examined whether an empathic chatbot can serve as a buffer against the adverse effects of social ostracism. After experiencing exclusion on social media, participants were randomly assigned to either talk with an empathetic chatbot about it (e.g., “I’m sorry that this happened to you”) or a control condition where their responses were merely acknowledged (e.g., “Thank you for your feedback”). Replicating previous research, results revealed that experiences of social exclusion dampened the mood of participants. Interacting with an empathetic chatbot, however, appeared to have a mitigating impact. In particular, participants in the chatbot intervention condition reported higher mood than those in the control condition. Theoretical, methodological, and practical implications, as well as directions for future research are discussed.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
From past research it is well known that social exclusion has detrimental consequences for mental health. To deal with these adverse effects, socially excluded individuals frequently turn to other humans for emotional support. While chatbots can elicit social and emotional responses on the part of the human interlocutor, their effectiveness in the context of social exclusion has not been investigated. In the present study, we examined whether an empathic chatbot can serve as a buffer against the adverse effects of social ostracism. After experiencing exclusion on social media, participants were randomly assigned to either talk with an empathetic chatbot about it (e.g., “I’m sorry that this happened to you”) or a control condition where their responses were merely acknowledged (e.g., “Thank you for your feedback”). Replicating previous research, results revealed that experiences of social exclusion dampened the mood of participants. Interacting with an empathetic chatbot, however, appeared to have a mitigating impact. In particular, participants in the chatbot intervention condition reported higher mood than those in the control condition. Theoretical, methodological, and practical implications, as well as directions for future research are discussed.
Zhao, Sicheng; Wang, Shangfei; Soleymani, Mohammad; Joshi, Dhiraj; Ji, Qiang
Affective Computing for Large-scale Heterogeneous Multimedia Data: A Survey Journal Article
In: ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 15, no. 3s, pp. 1–32, 2020, ISSN: 1551-6857, 1551-6865.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{zhao_affective_2020,
title = {Affective Computing for Large-scale Heterogeneous Multimedia Data: A Survey},
author = {Sicheng Zhao and Shangfei Wang and Mohammad Soleymani and Dhiraj Joshi and Qiang Ji},
url = {https://dl.acm.org/doi/10.1145/3363560},
doi = {10.1145/3363560},
issn = {1551-6857, 1551-6865},
year = {2020},
date = {2020-01-01},
journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
volume = {15},
number = {3s},
pages = {1–32},
abstract = {The wide popularity of digital photography and social networks has generated a rapidly growing volume of multimedia data (i.e., images, music, and videos), resulting in a great demand for managing, retrieving, and understanding these data. Affective computing (AC) of these data can help to understand human behaviors and enable wide applications. In this article, we survey the state-of-the-art AC technologies comprehensively for large-scale heterogeneous multimedia data. We begin this survey by introducing the typical emotion representation models from psychology that are widely employed in AC. We briefly describe the available datasets for evaluating AC algorithms. We then summarize and compare the representative methods on AC of different multimedia types, i.e., images, music, videos, and multimodal data, with the focus on both handcrafted features-based methods and deep learning methods. Finally, we discuss some challenges and future directions for multimedia affective computing.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
The wide popularity of digital photography and social networks has generated a rapidly growing volume of multimedia data (i.e., images, music, and videos), resulting in a great demand for managing, retrieving, and understanding these data. Affective computing (AC) of these data can help to understand human behaviors and enable wide applications. In this article, we survey the state-of-the-art AC technologies comprehensively for large-scale heterogeneous multimedia data. We begin this survey by introducing the typical emotion representation models from psychology that are widely employed in AC. We briefly describe the available datasets for evaluating AC algorithms. We then summarize and compare the representative methods on AC of different multimedia types, i.e., images, music, videos, and multimodal data, with the focus on both handcrafted features-based methods and deep learning methods. Finally, we discuss some challenges and future directions for multimedia affective computing.
2019
Rosenbloom, Paul S.; Joshi, Himanshu; Ustun, Volkan
(Sub)Symbolic × (a)symmetric × (non)combinatory: A map of AI approaches spanning symbolic/statistical to neural/ML Proceedings Article
In: Proceedings of the 7th Annual Conference on Advances in Cognitive Systems, pp. 113–131, Cognitive Systems Foundation, Cambridge, MA, 2019.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{rosenbloom_subsymbolic_2019,
title = {(Sub)Symbolic × (a)symmetric × (non)combinatory: A map of AI approaches spanning symbolic/statistical to neural/ML},
author = {Paul S. Rosenbloom and Himanshu Joshi and Volkan Ustun},
url = {https://drive.google.com/file/d/1Ynp75A048Mfuh7e3kf_V7hs5kFD7uHsT/view},
year = {2019},
date = {2019-12-01},
booktitle = {Proceedings of the 7th Annual Conference on Advances in Cognitive Systems},
pages = {113–131},
publisher = {Cognitive Systems Foundation},
address = {Cambridge, MA},
abstract = {The traditional symbolic versus subsymbolic dichotomy can be decomposed into three more basic dichotomies, to yield a 3D (2×2×2) space in which symbolic/statistical and neural/ML approaches to intelligence appear in opposite corners. Filling in all eight resulting cells then yields a map that spans a number of standard AI approaches plus a few that may be less familiar. Based on this map, four hypotheses are articulated, explored, and evaluated concerning its relevance to both a deeper understanding of the field of AI as a whole and the general capabilities required in complete AI/cognitive systems.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
The traditional symbolic versus subsymbolic dichotomy can be decomposed into three more basic dichotomies, to yield a 3D (2×2×2) space in which symbolic/statistical and neural/ML approaches to intelligence appear in opposite corners. Filling in all eight resulting cells then yields a map that spans a number of standard AI approaches plus a few that may be less familiar. Based on this map, four hypotheses are articulated, explored, and evaluated concerning its relevance to both a deeper understanding of the field of AI as a whole and the general capabilities required in complete AI/cognitive systems.
Hartholt, Arno; Fast, Ed; Reilly, Adam; Whitcup, Wendy; Liewer, Matt; Mozgai, Sharon
Ubiquitous Virtual Humans: A Multi-platform Framework for Embodied AI Agents in XR Proceedings Article
In: Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp. 308–3084, IEEE, San Diego, CA, USA, 2019, ISBN: 978-1-7281-5604-0.
Abstract | Links | BibTeX | Tags: UARC, VHTL, Virtual Humans
@inproceedings{hartholt_ubiquitous_2019,
title = {Ubiquitous Virtual Humans: A Multi-platform Framework for Embodied AI Agents in XR},
author = {Arno Hartholt and Ed Fast and Adam Reilly and Wendy Whitcup and Matt Liewer and Sharon Mozgai},
url = {https://ieeexplore.ieee.org/document/8942321/},
doi = {10.1109/AIVR46125.2019.00072},
isbn = {978-1-7281-5604-0},
year = {2019},
date = {2019-12-01},
booktitle = {Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)},
pages = {308–3084},
publisher = {IEEE},
address = {San Diego, CA, USA},
abstract = {We present an architecture and framework for the development of virtual humans for a range of computing platforms, including mobile, web, Virtual Reality (VR) and Augmented Reality (AR). The framework uses a mix of in-house and commodity technologies to support audio-visual sensing, speech recognition, natural language processing, nonverbal behavior generation and realization, text-to-speech generation, and rendering. This work builds on the Virtual Human Toolkit, which has been extended to support computing platforms beyond Windows. The resulting framework maintains the modularity of the underlying architecture, allows re-use of both logic and content through cloud services, and is extensible by porting lightweight clients. We present the current state of the framework, discuss how we model and animate our characters, and offer lessons learned through several use cases, including expressive character animation in seated VR, shared space and navigation in roomscale VR, autonomous AI in mobile AR, and real-time user performance feedback based on mobile sensors in headset AR.},
keywords = {UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
We present an architecture and framework for the development of virtual humans for a range of computing platforms, including mobile, web, Virtual Reality (VR) and Augmented Reality (AR). The framework uses a mix of in-house and commodity technologies to support audio-visual sensing, speech recognition, natural language processing, nonverbal behavior generation and realization, text-to-speech generation, and rendering. This work builds on the Virtual Human Toolkit, which has been extended to support computing platforms beyond Windows. The resulting framework maintains the modularity of the underlying architecture, allows re-use of both logic and content through cloud services, and is extensible by porting lightweight clients. We present the current state of the framework, discuss how we model and animate our characters, and offer lessons learned through several use cases, including expressive character animation in seated VR, shared space and navigation in roomscale VR, autonomous AI in mobile AR, and real-time user performance feedback based on mobile sensors in headset AR.
Chawla, Kushal; Srinivasan, Balaji Vasan; Chhaya, Niyati
Generating Formality-Tuned Summaries Using Input-Dependent Rewards Proceedings Article
In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pp. 833–842, Association for Computational Linguistics, Hong Kong, China, 2019.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{chawla_generating_2019,
title = {Generating Formality-Tuned Summaries Using Input-Dependent Rewards},
author = {Kushal Chawla and Balaji Vasan Srinivasan and Niyati Chhaya},
url = {https://www.aclweb.org/anthology/K19-1078},
doi = {10.18653/v1/K19-1078},
year = {2019},
date = {2019-11-01},
booktitle = {Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)},
pages = {833–842},
publisher = {Association for Computational Linguistics},
address = {Hong Kong, China},
abstract = {Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content. Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary. However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries. In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article. Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together. Once trained, the same model can generate formal and informal summary variants. Our automated and qualitative evaluations show the viability of the proposed framework.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content. Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary. However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries. In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article. Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together. Once trained, the same model can generate formal and informal summary variants. Our automated and qualitative evaluations show the viability of the proposed framework.
Tavabi, Leili; Stefanov, Kalin; Gilani, Setareh Nasihati; Traum, David; Soleymani, Mohammad
Multimodal Learning for Identifying Opportunities for Empathetic Responses Proceedings Article
In: Proceedings of the 2019 International Conference on Multimodal Interaction, pp. 95–104, ACM, Suzhou China, 2019, ISBN: 978-1-4503-6860-5.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{tavabi_multimodal_2019,
title = {Multimodal Learning for Identifying Opportunities for Empathetic Responses},
author = {Leili Tavabi and Kalin Stefanov and Setareh Nasihati Gilani and David Traum and Mohammad Soleymani},
url = {https://dl.acm.org/doi/10.1145/3340555.3353750},
doi = {10.1145/3340555.3353750},
isbn = {978-1-4503-6860-5},
year = {2019},
date = {2019-10-01},
booktitle = {Proceedings of the 2019 International Conference on Multimodal Interaction},
pages = {95–104},
publisher = {ACM},
address = {Suzhou China},
abstract = {Embodied interactive agents possessing emotional intelligence and empathy can create natural and engaging social interactions. Providing appropriate responses by interactive virtual agents requires the ability to perceive users’ emotional states. In this paper, we study and analyze behavioral cues that indicate an opportunity to provide an empathetic response. Emotional tone in language in addition to facial expressions are strong indicators of dramatic sentiment in conversation that warrant an empathetic response. To automatically recognize such instances, we develop a multimodal deep neural network for identifying opportunities when the agent should express positive or negative empathetic responses. We train and evaluate our model using audio, video and language from human-agent interactions in a wizard-of-Oz setting, using the wizard’s empathetic responses and annotations collected on Amazon Mechanical Turk as ground-truth labels. Our model outperforms a textbased baseline achieving F1-score of 0.71 on a three-class classification. We further investigate the results and evaluate the capability of such a model to be deployed for real-world human-agent interactions.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Embodied interactive agents possessing emotional intelligence and empathy can create natural and engaging social interactions. Providing appropriate responses by interactive virtual agents requires the ability to perceive users’ emotional states. In this paper, we study and analyze behavioral cues that indicate an opportunity to provide an empathetic response. Emotional tone in language in addition to facial expressions are strong indicators of dramatic sentiment in conversation that warrant an empathetic response. To automatically recognize such instances, we develop a multimodal deep neural network for identifying opportunities when the agent should express positive or negative empathetic responses. We train and evaluate our model using audio, video and language from human-agent interactions in a wizard-of-Oz setting, using the wizard’s empathetic responses and annotations collected on Amazon Mechanical Turk as ground-truth labels. Our model outperforms a textbased baseline achieving F1-score of 0.71 on a three-class classification. We further investigate the results and evaluate the capability of such a model to be deployed for real-world human-agent interactions.
Haring, Kerstin S.; Tobias, Jessica; Waligora, Justin; Phillips, Elizabeth; Tenhundfeld, Nathan L; LUCAS, Gale; Visser, Ewart J; GRATCH, Jonathan; Tossell, Chad
Conflict Mediation in Human-Machine Teaming: Using a Virtual Agent to Support Mission Planning and Debriefing Proceedings Article
In: Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, New Delhi, India, 2019.
Abstract | Links | BibTeX | Tags: Virtual Humans
@inproceedings{haring_conflict_2019,
title = {Conflict Mediation in Human-Machine Teaming: Using a Virtual Agent to Support Mission Planning and Debriefing},
author = {Kerstin S. Haring and Jessica Tobias and Justin Waligora and Elizabeth Phillips and Nathan L Tenhundfeld and Gale LUCAS and Ewart J Visser and Jonathan GRATCH and Chad Tossell},
url = {https://ieeexplore.ieee.org/abstract/document/8956414},
doi = {10.1109/RO-MAN46459.2019.8956414},
year = {2019},
date = {2019-10-01},
booktitle = {Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
address = {New Delhi, India},
abstract = {Socially intelligent artificial agents and robots are anticipated to become ubiquitous in home, work, and military environments. With the addition of such agents to human teams it is crucial to evaluate their role in the planning, decision making, and conflict mediation processes. We conducted a study to evaluate the utility of a virtual agent that provided mission planning support in a three-person human team during a military strategic mission planning scenario. The team consisted of a human team lead who made the final decisions and three supporting roles, two humans and the artificial agent. The mission outcome was experimentally designed to fail and introduced a conflict between the human team members and the leader. This conflict was mediated by the artificial agent during the debriefing process through discuss or debate and open communication strategies of conflict resolution [1]. Our results showed that our teams experienced conflict. The teams also responded socially to the virtual agent, although they did not find the agent beneficial to the mediation process. Finally, teams collaborated well together and perceived task proficiency increased for team leaders. Socially intelligent agents show potential for conflict mediation, but need careful design and implementation to improve team processes and collaboration.},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Socially intelligent artificial agents and robots are anticipated to become ubiquitous in home, work, and military environments. With the addition of such agents to human teams it is crucial to evaluate their role in the planning, decision making, and conflict mediation processes. We conducted a study to evaluate the utility of a virtual agent that provided mission planning support in a three-person human team during a military strategic mission planning scenario. The team consisted of a human team lead who made the final decisions and three supporting roles, two humans and the artificial agent. The mission outcome was experimentally designed to fail and introduced a conflict between the human team members and the leader. This conflict was mediated by the artificial agent during the debriefing process through discuss or debate and open communication strategies of conflict resolution [1]. Our results showed that our teams experienced conflict. The teams also responded socially to the virtual agent, although they did not find the agent beneficial to the mediation process. Finally, teams collaborated well together and perceived task proficiency increased for team leaders. Socially intelligent agents show potential for conflict mediation, but need careful design and implementation to improve team processes and collaboration.
Soleymani, Mohammad; Stefanov, Kalin; Kang, Sin-Hwa; Ondras, Jan; Gratch, Jonathan
Multimodal Analysis and Estimation of Intimate Self-Disclosure Proceedings Article
In: Proceedings of the 2019 International Conference on Multimodal Interaction on - ICMI '19, pp. 59–68, ACM Press, Suzhou, China, 2019, ISBN: 978-1-4503-6860-5.
Abstract | Links | BibTeX | Tags: MxR, UARC, Virtual Humans
@inproceedings{soleymani_multimodal_2019,
title = {Multimodal Analysis and Estimation of Intimate Self-Disclosure},
author = {Mohammad Soleymani and Kalin Stefanov and Sin-Hwa Kang and Jan Ondras and Jonathan Gratch},
url = {http://dl.acm.org/citation.cfm?doid=3340555.3353737},
doi = {10.1145/3340555.3353737},
isbn = {978-1-4503-6860-5},
year = {2019},
date = {2019-10-01},
booktitle = {Proceedings of the 2019 International Conference on Multimodal Interaction on - ICMI '19},
pages = {59–68},
publisher = {ACM Press},
address = {Suzhou, China},
abstract = {Self-disclosure to others has a proven benefit for one’s mental health. It is shown that disclosure to computers can be similarly beneficial for emotional and psychological well-being. In this paper, we analyzed verbal and nonverbal behavior associated with self-disclosure in two datasets containing structured human-human and human-agent interviews from more than 200 participants. Correlation analysis of verbal and nonverbal behavior revealed that linguistic features such as affective and cognitive content in verbal behavior, and nonverbal behavior such as head gestures are associated with intimate self-disclosure. A multimodal deep neural network was developed to automatically estimate the level of intimate self-disclosure from verbal and nonverbal behavior. Between modalities, verbal behavior was the best modality for estimating self-disclosure within-corpora achieving r = 0.66. However, the cross-corpus evaluation demonstrated that nonverbal behavior can outperform language modality in cross-corpus evaluation. Such automatic models can be deployed in interactive virtual agents or social robots to evaluate rapport and guide their conversational strategy.},
keywords = {MxR, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Self-disclosure to others has a proven benefit for one’s mental health. It is shown that disclosure to computers can be similarly beneficial for emotional and psychological well-being. In this paper, we analyzed verbal and nonverbal behavior associated with self-disclosure in two datasets containing structured human-human and human-agent interviews from more than 200 participants. Correlation analysis of verbal and nonverbal behavior revealed that linguistic features such as affective and cognitive content in verbal behavior, and nonverbal behavior such as head gestures are associated with intimate self-disclosure. A multimodal deep neural network was developed to automatically estimate the level of intimate self-disclosure from verbal and nonverbal behavior. Between modalities, verbal behavior was the best modality for estimating self-disclosure within-corpora achieving r = 0.66. However, the cross-corpus evaluation demonstrated that nonverbal behavior can outperform language modality in cross-corpus evaluation. Such automatic models can be deployed in interactive virtual agents or social robots to evaluate rapport and guide their conversational strategy.
Ringeval, Fabien; Messner, Eva-Maria; Song, Siyang; Liu, Shuo; Zhao, Ziping; Mallol-Ragolta, Adria; Ren, Zhao; Soleymani, Mohammad; Pantic, Maja; Schuller, Björn; Valstar, Michel; Cummins, Nicholas; Cowie, Roddy; Tavabi, Leili; Schmitt, Maximilian; Alisamir, Sina; Amiriparian, Shahin
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition Proceedings Article
In: Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop - AVEC '19, pp. 3–12, ACM Press, Nice, France, 2019, ISBN: 978-1-4503-6913-8.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@inproceedings{ringeval_avec_2019,
title = {AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition},
author = {Fabien Ringeval and Eva-Maria Messner and Siyang Song and Shuo Liu and Ziping Zhao and Adria Mallol-Ragolta and Zhao Ren and Mohammad Soleymani and Maja Pantic and Björn Schuller and Michel Valstar and Nicholas Cummins and Roddy Cowie and Leili Tavabi and Maximilian Schmitt and Sina Alisamir and Shahin Amiriparian},
url = {http://dl.acm.org/citation.cfm?doid=3347320.3357688},
doi = {10.1145/3347320.3357688},
isbn = {978-1-4503-6913-8},
year = {2019},
date = {2019-10-01},
booktitle = {Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop - AVEC '19},
pages = {3–12},
publisher = {ACM Press},
address = {Nice, France},
abstract = {The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) 'State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition' is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) 'State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition' is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.