Publications
Search
1.
Kwon, Deuksin; Weiss, Emily; Kulshrestha, Tara; Chawla, Kushal; Lucas, Gale M.; Gratch, Jonathan
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues Miscellaneous
2024, (arXiv:2402.13550 [cs]).
@misc{kwon_are_2024,
title = {Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues},
author = {Deuksin Kwon and Emily Weiss and Tara Kulshrestha and Kushal Chawla and Gale M. Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2402.13550},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-14},
publisher = {arXiv},
abstract = {A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.},
note = {arXiv:2402.13550 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.
2.
Murawski, Alaine; Ramirez‐Zohfeld, Vanessa; Mell, Johnathan; Tschoe, Marianne; Schierer, Allison; Olvera, Charles; Brett, Jeanne; Gratch, Jonathan; Lindquist, Lee A.
Development and pilot testing of an artificial intelligence‐based family caregiver negotiation program Journal Article
In: J American Geriatrics Society, pp. jgs.18775, 2024, ISSN: 0002-8614, 1532-5415.
@article{murawski_development_2024,
title = {Development and pilot testing of an artificial intelligence‐based family caregiver negotiation program},
author = {Alaine Murawski and Vanessa Ramirez‐Zohfeld and Johnathan Mell and Marianne Tschoe and Allison Schierer and Charles Olvera and Jeanne Brett and Jonathan Gratch and Lee A. Lindquist},
url = {https://agsjournals.onlinelibrary.wiley.com/doi/10.1111/jgs.18775},
doi = {10.1111/jgs.18775},
issn = {0002-8614, 1532-5415},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
journal = {J American Geriatrics Society},
pages = {jgs.18775},
abstract = {Abstract
Background
Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial‐intelligence negotiation training program, NegotiAge, for family caregivers.
Methods
We convened negotiation experts, a geriatrician, a social worker, and community‐based family caregivers. Content matter experts created short videos to teach negotiation skills. Caregivers generated dialogue surrounding conflicts. Computer scientists utilized the dialogue with the Interactive Arbitration Guide Online (IAGO) platform to develop avatar‐based agents (e.g., sibling, older adult, physician) for caregivers to practice negotiating. Pilot testing was conducted with family caregivers to assess usability (USE) and satisfaction (open‐ended questions with thematic analysis).
Results
Development: With NegotiAge, caregivers progress through didactic material, then receive scenarios to negotiate (e.g., physician recommends gastric tube, sibling disagrees with home support, older adult refusing support). Caregivers negotiate in real‐time with avatars who are designed to act like humans, including emotional tactics and irrational behaviors. Caregivers send/receive offers, using tactics until either mutual agreement or time expires. Immediate feedback is generated for the user to improve skills training. Pilot testing: Family caregivers (
n = 12) completed the program and survey. USE questionnaire (Likert scale 1–7) subset scores revealed: (1) Useful—Mean 5.69 (SD 0.76); (2) Ease—Mean 5.24 (SD 0.96); (3) Learn—Mean 5.69 (SD 0.74); (4) Satisfy—Mean 5.62 (SD 1.10). Items that received over 80% agreements were: It helps me be more effective; It helps me be more productive; It is useful; It gives me more control over the activities in my life; It makes the things I want to accomplish easier to get done. Participants were highly satisfied and found NegotiAge fun to use (91.7%), with 100% who would recommend it to a friend.
Conclusion
NegotiAge is an Artificial‐Intelligent Caregiver Negotiation Program, that is usable and feasible for family caregivers to become familiar with negotiating conflicts commonly seen in health care.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
Background
Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial‐intelligence negotiation training program, NegotiAge, for family caregivers.
Methods
We convened negotiation experts, a geriatrician, a social worker, and community‐based family caregivers. Content matter experts created short videos to teach negotiation skills. Caregivers generated dialogue surrounding conflicts. Computer scientists utilized the dialogue with the Interactive Arbitration Guide Online (IAGO) platform to develop avatar‐based agents (e.g., sibling, older adult, physician) for caregivers to practice negotiating. Pilot testing was conducted with family caregivers to assess usability (USE) and satisfaction (open‐ended questions with thematic analysis).
Results
Development: With NegotiAge, caregivers progress through didactic material, then receive scenarios to negotiate (e.g., physician recommends gastric tube, sibling disagrees with home support, older adult refusing support). Caregivers negotiate in real‐time with avatars who are designed to act like humans, including emotional tactics and irrational behaviors. Caregivers send/receive offers, using tactics until either mutual agreement or time expires. Immediate feedback is generated for the user to improve skills training. Pilot testing: Family caregivers (
n = 12) completed the program and survey. USE questionnaire (Likert scale 1–7) subset scores revealed: (1) Useful—Mean 5.69 (SD 0.76); (2) Ease—Mean 5.24 (SD 0.96); (3) Learn—Mean 5.69 (SD 0.74); (4) Satisfy—Mean 5.62 (SD 1.10). Items that received over 80% agreements were: It helps me be more effective; It helps me be more productive; It is useful; It gives me more control over the activities in my life; It makes the things I want to accomplish easier to get done. Participants were highly satisfied and found NegotiAge fun to use (91.7%), with 100% who would recommend it to a friend.
Conclusion
NegotiAge is an Artificial‐Intelligent Caregiver Negotiation Program, that is usable and feasible for family caregivers to become familiar with negotiating conflicts commonly seen in health care.
Background
Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial‐intelligence negotiation training program, NegotiAge, for family caregivers.
Methods
We convened negotiation experts, a geriatrician, a social worker, and community‐based family caregivers. Content matter experts created short videos to teach negotiation skills. Caregivers generated dialogue surrounding conflicts. Computer scientists utilized the dialogue with the Interactive Arbitration Guide Online (IAGO) platform to develop avatar‐based agents (e.g., sibling, older adult, physician) for caregivers to practice negotiating. Pilot testing was conducted with family caregivers to assess usability (USE) and satisfaction (open‐ended questions with thematic analysis).
Results
Development: With NegotiAge, caregivers progress through didactic material, then receive scenarios to negotiate (e.g., physician recommends gastric tube, sibling disagrees with home support, older adult refusing support). Caregivers negotiate in real‐time with avatars who are designed to act like humans, including emotional tactics and irrational behaviors. Caregivers send/receive offers, using tactics until either mutual agreement or time expires. Immediate feedback is generated for the user to improve skills training. Pilot testing: Family caregivers (
n = 12) completed the program and survey. USE questionnaire (Likert scale 1–7) subset scores revealed: (1) Useful—Mean 5.69 (SD 0.76); (2) Ease—Mean 5.24 (SD 0.96); (3) Learn—Mean 5.69 (SD 0.74); (4) Satisfy—Mean 5.62 (SD 1.10). Items that received over 80% agreements were: It helps me be more effective; It helps me be more productive; It is useful; It gives me more control over the activities in my life; It makes the things I want to accomplish easier to get done. Participants were highly satisfied and found NegotiAge fun to use (91.7%), with 100% who would recommend it to a friend.
Conclusion
NegotiAge is an Artificial‐Intelligent Caregiver Negotiation Program, that is usable and feasible for family caregivers to become familiar with negotiating conflicts commonly seen in health care.
3.
Gurney, Nikolos; Pynadath, David V.; Ustun, Volkan
Spontaneous Theory of Mind for Artificial Intelligence Journal Article
In: 2024, (Publisher: [object Object] Version Number: 1).
@article{gurney_spontaneous_2024,
title = {Spontaneous Theory of Mind for Artificial Intelligence},
author = {Nikolos Gurney and David V. Pynadath and Volkan Ustun},
url = {https://arxiv.org/abs/2402.13272},
doi = {10.48550/ARXIV.2402.13272},
year = {2024},
date = {2024-01-01},
urldate = {2024-03-14},
abstract = {Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM – reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts textbackslashtextitand spontaneously engage in social reasoning.},
note = {Publisher: [object Object]
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM – reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts textbackslashtextitand spontaneously engage in social reasoning.
4.
Ustun, Volkan; Jorvekar, Ronit; Gurney, Nikolos; Pynadath, David; Wang, Yunzhe
Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent: Proceedings Article
In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, pp. 313–320, SCITEPRESS - Science and Technology Publications, Rome, Italy, 2024, ISBN: 978-989-758-680-4.
@inproceedings{ustun_assessing_2024,
title = {Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent:},
author = {Volkan Ustun and Ronit Jorvekar and Nikolos Gurney and David Pynadath and Yunzhe Wang},
url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0012388100003636},
doi = {10.5220/0012388100003636},
isbn = {978-989-758-680-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-03-19},
booktitle = {Proceedings of the 16th International Conference on Agents and Artificial Intelligence},
pages = {313–320},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Rome, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
5.
Yang, Daniel; Kommineni, Aditya; Alshehri, Mohammad; Mohanty, Nilamadhab; Modi, Vedant; Gratch, Jonathan; Narayanan, Shrikanth
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models Miscellaneous
2023, (arXiv:2311.03551 [cs]).
@misc{yang_context_2023,
title = {Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models},
author = {Daniel Yang and Aditya Kommineni and Mohammad Alshehri and Nilamadhab Mohanty and Vedant Modi and Jonathan Gratch and Shrikanth Narayanan},
url = {http://arxiv.org/abs/2311.03551},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.},
note = {arXiv:2311.03551 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.
6.
Cho, Hyundong; Liu, Shuai; Shi, Taiwei; Jain, Darpan; Rizk, Basem; Huang, Yuyang; Lu, Zixun; Wen, Nuan; Gratch, Jonathan; Ferrara, Emilio; May, Jonathan
Can Language Model Moderators Improve the Health of Online Discourse? Miscellaneous
2023, (arXiv:2311.10781 [cs]).
@misc{cho_can_2023,
title = {Can Language Model Moderators Improve the Health of Online Discourse?},
author = {Hyundong Cho and Shuai Liu and Taiwei Shi and Darpan Jain and Basem Rizk and Yuyang Huang and Zixun Lu and Nuan Wen and Jonathan Gratch and Emilio Ferrara and Jonathan May},
url = {http://arxiv.org/abs/2311.10781},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.},
note = {arXiv:2311.10781 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.
7.
Andrist, Sean; Bohus, Dan; Li, Zongjian; Soleymani, Mohammad
Platform for Situated Intelligence and OpenSense: A Tutorial on Building Multimodal Interactive Applications for Research Proceedings Article
In: International Cconference on Multimodal Interaction, pp. 105–106, ACM, Paris France, 2023, ISBN: 9798400703218.
@inproceedings{andrist_platform_2023,
title = {Platform for Situated Intelligence and OpenSense: A Tutorial on Building Multimodal Interactive Applications for Research},
author = {Sean Andrist and Dan Bohus and Zongjian Li and Mohammad Soleymani},
url = {https://dl.acm.org/doi/10.1145/3610661.3617603},
doi = {10.1145/3610661.3617603},
isbn = {9798400703218},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
booktitle = {International Cconference on Multimodal Interaction},
pages = {105–106},
publisher = {ACM},
address = {Paris France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
8.
Prinzing, Michael; Garton, Catherine; Berman, Catherine J.; Zhou, Jieni; West, Taylor Nicole; Gratch, Jonathan; Fredrickson, Barbara
Can AI Agents Help Humans to Connect? Technical Report
PsyArXiv 2023.
@techreport{prinzing_can_2023,
title = {Can AI Agents Help Humans to Connect?},
author = {Michael Prinzing and Catherine Garton and Catherine J. Berman and Jieni Zhou and Taylor Nicole West and Jonathan Gratch and Barbara Fredrickson},
url = {https://osf.io/muq6s},
doi = {10.31234/osf.io/muq6s},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
institution = {PsyArXiv},
abstract = {This paper reports on a pre-registered experiment designed to test whether artificial agents can help people to create more moments of high-quality connection with other humans. Of four pre-registered hypotheses, we found (partial) support for only one.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
This paper reports on a pre-registered experiment designed to test whether artificial agents can help people to create more moments of high-quality connection with other humans. Of four pre-registered hypotheses, we found (partial) support for only one.
9.
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.
10.
Gurney, Nikolos; Miller, John H.; Pynadath, David V.
The Role of Heuristics and Biases during Complex Choices with an AI Teammate Journal Article
In: AAAI, vol. 37, no. 5, pp. 5993–6001, 2023, ISSN: 2374-3468, 2159-5399.
@article{gurney_role_2023,
title = {The Role of Heuristics and Biases during Complex Choices with an AI Teammate},
author = {Nikolos Gurney and John H. Miller and David V. Pynadath},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/25741},
doi = {10.1609/aaai.v37i5.25741},
issn = {2374-3468, 2159-5399},
year = {2023},
date = {2023-06-01},
urldate = {2023-12-08},
journal = {AAAI},
volume = {37},
number = {5},
pages = {5993–6001},
abstract = {Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating AI helper usage. AI assistance is, arguably, most valuable when humans must make complex choices. We argue that classic experimental methods used to study heuristics and biases are insufficient for studying complex choices made with AI helpers. We adapted an experimental paradigm designed for studying complex choices in such contexts. We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes. The evidence suggests that some participants, particularly those in a loss frame, put too much faith in the AI helper and experienced worse choice outcomes by doing so. The paradigm also generates computational modeling-friendly data allowing future studies of human-AI decision making.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating AI helper usage. AI assistance is, arguably, most valuable when humans must make complex choices. We argue that classic experimental methods used to study heuristics and biases are insufficient for studying complex choices made with AI helpers. We adapted an experimental paradigm designed for studying complex choices in such contexts. We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes. The evidence suggests that some participants, particularly those in a loss frame, put too much faith in the AI helper and experienced worse choice outcomes by doing so. The paradigm also generates computational modeling-friendly data allowing future studies of human-AI decision making.
11.
Wu, Haochen; Sequeira, Pedro; Pynadath, David V.
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning Journal Article
In: 2023, (Publisher: arXiv Version Number: 2).
@article{wu_multiagent_2023,
title = {Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning},
author = {Haochen Wu and Pedro Sequeira and David V. Pynadath},
url = {https://arxiv.org/abs/2302.10238},
doi = {10.48550/ARXIV.2302.10238},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-24},
abstract = {We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.},
note = {Publisher: arXiv
Version Number: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.
12.
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 = {2023-01-01},
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}
}
13.
Gurney, Nikolos; Pynadath, David V.; Wang, Ning
Comparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions Book Section
In: vol. 13832, pp. 175–197, 2023, (arXiv:2302.01854 [cs]).
@incollection{gurney_comparing_2023,
title = {Comparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions},
author = {Nikolos Gurney and David V. Pynadath and Ning Wang},
url = {http://arxiv.org/abs/2302.01854},
doi = {10.1007/978-3-031-30933-5_12},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-15},
volume = {13832},
pages = {175–197},
abstract = {Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differences in people's predisposition to trust can significantly impact their likelihood of complying with recommendations from the AI. Predisposition to trust is often measured with self-report inventories that are administered before interactions. We benchmark a popular measure of this kind against behavioral predictors of compliance. We find that the inventory is a less effective predictor of compliance than the behavioral measures in datasets taken from three previous research projects. This suggests a general property that individual differences in initial behavior are more predictive than differences in self-reported trust attitudes. This result also shows a potential for easily accessible behavioral measures to provide an AI with more accurate models without the use of (often costly) survey instruments.},
note = {arXiv:2302.01854 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differences in people's predisposition to trust can significantly impact their likelihood of complying with recommendations from the AI. Predisposition to trust is often measured with self-report inventories that are administered before interactions. We benchmark a popular measure of this kind against behavioral predictors of compliance. We find that the inventory is a less effective predictor of compliance than the behavioral measures in datasets taken from three previous research projects. This suggests a general property that individual differences in initial behavior are more predictive than differences in self-reported trust attitudes. This result also shows a potential for easily accessible behavioral measures to provide an AI with more accurate models without the use of (often costly) survey instruments.
14.
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 = {2023-01-01},
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}
}
15.
Gratch, Jonathan; Fast, Nathanael J.
The power to harm: AI assistants pave the way to unethical behavior Journal Article
In: Current Opinion in Psychology, vol. 47, pp. 101382, 2022, ISSN: 2352250X.
@article{gratch_power_2022,
title = {The power to harm: AI assistants pave the way to unethical behavior},
author = {Jonathan Gratch and Nathanael J. Fast},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2352250X22001014},
doi = {10.1016/j.copsyc.2022.101382},
issn = {2352250X},
year = {2022},
date = {2022-10-01},
urldate = {2022-09-28},
journal = {Current Opinion in Psychology},
volume = {47},
pages = {101382},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
16.
Hartholt, Arno; Fast, Ed; Leeds, Andrew; Kim, Kevin; Gordon, Andrew; McCullough, Kyle; Ustun, Volkan; Mozgai, Sharon
Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities Proceedings Article
In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp. 1902–1904, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2022, ISBN: 978-1-4503-9213-6.
@inproceedings{hartholt_demonstrating_2022,
title = {Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities},
author = {Arno Hartholt and Ed Fast and Andrew Leeds and Kevin Kim and Andrew Gordon and Kyle McCullough and Volkan Ustun and Sharon Mozgai},
isbn = {978-1-4503-9213-6},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-20},
booktitle = {Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems},
pages = {1902–1904},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
series = {AAMAS '22},
abstract = {We demonstrate the Rapid Integration & Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We demonstrate the Rapid Integration & Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.
17.
Paun, Silviu; Artstein, Ron; Poesio, Massimo
Statistical Methods for Annotation Analysis Book
Springer International Publishing, Cham, 2022, ISBN: 978-3-031-03753-5 978-3-031-03763-4.
@book{paun_statistical_2022,
title = {Statistical Methods for Annotation Analysis},
author = {Silviu Paun and Ron Artstein and Massimo Poesio},
url = {https://link.springer.com/10.1007/978-3-031-03763-4},
doi = {10.1007/978-3-031-03763-4},
isbn = {978-3-031-03753-5 978-3-031-03763-4},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-28},
publisher = {Springer International Publishing},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
18.
Wang, Ning; Greenwald, Eric; Montgomery, Ryan; Leitner, Maxyn
ARIN-561: An Educational Game for Learning Artificial Intelligence for High-School Students Proceedings Article
In: Rodrigo, Maria Mercedes; Matsuda, Noburu; Cristea, Alexandra I.; Dimitrova, Vania (Ed.): Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, pp. 528–531, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-11647-6.
@inproceedings{wang_arin-561_2022,
title = {ARIN-561: An Educational Game for Learning Artificial Intelligence for High-School Students},
author = {Ning Wang and Eric Greenwald and Ryan Montgomery and Maxyn Leitner},
editor = {Maria Mercedes Rodrigo and Noburu Matsuda and Alexandra I. Cristea and Vania Dimitrova},
url = {https://link.springer.com/chapter/10.1007/978-3-031-11647-6_108},
doi = {10.1007/978-3-031-11647-6_108},
isbn = {978-3-031-11647-6},
year = {2022},
date = {2022-01-01},
booktitle = {Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium},
pages = {528–531},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Artificial Intelligence (AI) is increasingly vital to our future generations, who will join a workforce that utilizes AI-driven tools and contributes to the advancement of AI. Today’s students will need exposure to AI knowledge at a younger age. Relatively little is currently known about how to most effectively provide AI education to K-12 students. In this paper, we discuss the design and evaluation of an educational game for high-school AI education called ARIN-561. Results from pilot studies indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Artificial Intelligence (AI) is increasingly vital to our future generations, who will join a workforce that utilizes AI-driven tools and contributes to the advancement of AI. Today’s students will need exposure to AI knowledge at a younger age. Relatively little is currently known about how to most effectively provide AI education to K-12 students. In this paper, we discuss the design and evaluation of an educational game for high-school AI education called ARIN-561. Results from pilot studies indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.
19.
Gurney, Nikolos; Pynadath, David V.; Wang, Ning
Measuring and Predicting Human Trust in Recommendations from an AI Teammate Proceedings Article
In: Degen, Helmut; Ntoa, Stavroula (Ed.): Artificial Intelligence in HCI, pp. 22–34, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-05643-7.
@inproceedings{gurney_measuring_2022,
title = {Measuring and Predicting Human Trust in Recommendations from an AI Teammate},
author = {Nikolos Gurney and David V. Pynadath and Ning Wang},
editor = {Helmut Degen and Stavroula Ntoa},
url = {https://link.springer.com/chapter/10.1007/978-3-031-05643-7_2},
doi = {10.1007/978-3-031-05643-7_2},
isbn = {978-3-031-05643-7},
year = {2022},
date = {2022-01-01},
booktitle = {Artificial Intelligence in HCI},
pages = {22–34},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Predicting compliance with AI recommendations and knowing when to intervene are critical facets of human-AI teaming. AIs are typically deployed in settings where their abilities to evaluate decision variables far exceed the abilities of their human counterparts. However, even though AIs excel at weighing multiple issues and computing near optimal solutions with speed and accuracy beyond that of any human, they still make mistakes. Thus, perfect compliance may be undesirable. This means, just as individuals must know when to follow the advice of other people, it is critical for them to know when to adopt the recommendations from their AI. Well-calibrated trust is thought to be a fundamental aspect of this type of knowledge. We compare the ability of a common trust inventory and the ability of a behavioral measure of trust to predict compliance and success in a reconnaissance mission. We interpret the experimental results to suggest that the behavioral measure is a better predictor of overall mission compliance and success. We discuss how this measure could possibly be used in compliance interventions and related open questions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Predicting compliance with AI recommendations and knowing when to intervene are critical facets of human-AI teaming. AIs are typically deployed in settings where their abilities to evaluate decision variables far exceed the abilities of their human counterparts. However, even though AIs excel at weighing multiple issues and computing near optimal solutions with speed and accuracy beyond that of any human, they still make mistakes. Thus, perfect compliance may be undesirable. This means, just as individuals must know when to follow the advice of other people, it is critical for them to know when to adopt the recommendations from their AI. Well-calibrated trust is thought to be a fundamental aspect of this type of knowledge. We compare the ability of a common trust inventory and the ability of a behavioral measure of trust to predict compliance and success in a reconnaissance mission. We interpret the experimental results to suggest that the behavioral measure is a better predictor of overall mission compliance and success. We discuss how this measure could possibly be used in compliance interventions and related open questions.
20.
Leitner, Maxyn; Greenwald, Eric; Montgomery, Ryan; Wang, Ning
Design and Evaluation of ARIN-561: An Educational Game for Youth Artificial Intelligence Education Proceedings Article
In: Proceedings of the 30th International Conference on Computers in Education, 2022.
@inproceedings{leitner_design_2022,
title = {Design and Evaluation of ARIN-561: An Educational Game for Youth Artificial Intelligence Education},
author = {Maxyn Leitner and Eric Greenwald and Ryan Montgomery and Ning Wang},
url = {https://par.nsf.gov/servlets/purl/10440195},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the 30th International Conference on Computers in Education},
abstract = {Artificial Intelligence (AI) is increasingly vital to our everyday lives. Future generations will not only consume AI, but work with AI-driven tools and contribute to the development of AI. As such, students will need exposure to AI knowledge at a younger age. Despite this need, relatively little is currently known about how to most effectively provide AI education to K-12 (kindergarten through 12th grade) students. In this paper, we discuss the design of an educational game for high-school AI education called ARIN-561. The game centered around two agents – a player character and a companion robot, as the story and learning experience unfold through conversations between the two agents and explorations that bond the two agents A series of studies were carried out at high schools in the United States to evaluate the efficacy of the game. Results indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Artificial Intelligence (AI) is increasingly vital to our everyday lives. Future generations will not only consume AI, but work with AI-driven tools and contribute to the development of AI. As such, students will need exposure to AI knowledge at a younger age. Despite this need, relatively little is currently known about how to most effectively provide AI education to K-12 (kindergarten through 12th grade) students. In this paper, we discuss the design of an educational game for high-school AI education called ARIN-561. The game centered around two agents – a player character and a companion robot, as the story and learning experience unfold through conversations between the two agents and explorations that bond the two agents A series of studies were carried out at high schools in the United States to evaluate the efficacy of the game. Results indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.
Filter
2024
Kwon, Deuksin; Weiss, Emily; Kulshrestha, Tara; Chawla, Kushal; Lucas, Gale M.; Gratch, Jonathan
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues Miscellaneous
2024, (arXiv:2402.13550 [cs]).
Abstract | Links | BibTeX | Tags: AI, Virtual Humans
@misc{kwon_are_2024,
title = {Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues},
author = {Deuksin Kwon and Emily Weiss and Tara Kulshrestha and Kushal Chawla and Gale M. Lucas and Jonathan Gratch},
url = {http://arxiv.org/abs/2402.13550},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-14},
publisher = {arXiv},
abstract = {A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.},
note = {arXiv:2402.13550 [cs]},
keywords = {AI, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.
Murawski, Alaine; Ramirez‐Zohfeld, Vanessa; Mell, Johnathan; Tschoe, Marianne; Schierer, Allison; Olvera, Charles; Brett, Jeanne; Gratch, Jonathan; Lindquist, Lee A.
Development and pilot testing of an artificial intelligence‐based family caregiver negotiation program Journal Article
In: J American Geriatrics Society, pp. jgs.18775, 2024, ISSN: 0002-8614, 1532-5415.
Abstract | Links | BibTeX | Tags: AI, Virtual Humans
@article{murawski_development_2024,
title = {Development and pilot testing of an artificial intelligence‐based family caregiver negotiation program},
author = {Alaine Murawski and Vanessa Ramirez‐Zohfeld and Johnathan Mell and Marianne Tschoe and Allison Schierer and Charles Olvera and Jeanne Brett and Jonathan Gratch and Lee A. Lindquist},
url = {https://agsjournals.onlinelibrary.wiley.com/doi/10.1111/jgs.18775},
doi = {10.1111/jgs.18775},
issn = {0002-8614, 1532-5415},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
journal = {J American Geriatrics Society},
pages = {jgs.18775},
abstract = {Abstract
Background
Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial‐intelligence negotiation training program, NegotiAge, for family caregivers.
Methods
We convened negotiation experts, a geriatrician, a social worker, and community‐based family caregivers. Content matter experts created short videos to teach negotiation skills. Caregivers generated dialogue surrounding conflicts. Computer scientists utilized the dialogue with the Interactive Arbitration Guide Online (IAGO) platform to develop avatar‐based agents (e.g., sibling, older adult, physician) for caregivers to practice negotiating. Pilot testing was conducted with family caregivers to assess usability (USE) and satisfaction (open‐ended questions with thematic analysis).
Results
Development: With NegotiAge, caregivers progress through didactic material, then receive scenarios to negotiate (e.g., physician recommends gastric tube, sibling disagrees with home support, older adult refusing support). Caregivers negotiate in real‐time with avatars who are designed to act like humans, including emotional tactics and irrational behaviors. Caregivers send/receive offers, using tactics until either mutual agreement or time expires. Immediate feedback is generated for the user to improve skills training. Pilot testing: Family caregivers (
n = 12) completed the program and survey. USE questionnaire (Likert scale 1–7) subset scores revealed: (1) Useful—Mean 5.69 (SD 0.76); (2) Ease—Mean 5.24 (SD 0.96); (3) Learn—Mean 5.69 (SD 0.74); (4) Satisfy—Mean 5.62 (SD 1.10). Items that received over 80% agreements were: It helps me be more effective; It helps me be more productive; It is useful; It gives me more control over the activities in my life; It makes the things I want to accomplish easier to get done. Participants were highly satisfied and found NegotiAge fun to use (91.7%), with 100% who would recommend it to a friend.
Conclusion
NegotiAge is an Artificial‐Intelligent Caregiver Negotiation Program, that is usable and feasible for family caregivers to become familiar with negotiating conflicts commonly seen in health care.},
keywords = {AI, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Abstract
Background
Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial‐intelligence negotiation training program, NegotiAge, for family caregivers.
Methods
We convened negotiation experts, a geriatrician, a social worker, and community‐based family caregivers. Content matter experts created short videos to teach negotiation skills. Caregivers generated dialogue surrounding conflicts. Computer scientists utilized the dialogue with the Interactive Arbitration Guide Online (IAGO) platform to develop avatar‐based agents (e.g., sibling, older adult, physician) for caregivers to practice negotiating. Pilot testing was conducted with family caregivers to assess usability (USE) and satisfaction (open‐ended questions with thematic analysis).
Results
Development: With NegotiAge, caregivers progress through didactic material, then receive scenarios to negotiate (e.g., physician recommends gastric tube, sibling disagrees with home support, older adult refusing support). Caregivers negotiate in real‐time with avatars who are designed to act like humans, including emotional tactics and irrational behaviors. Caregivers send/receive offers, using tactics until either mutual agreement or time expires. Immediate feedback is generated for the user to improve skills training. Pilot testing: Family caregivers (
n = 12) completed the program and survey. USE questionnaire (Likert scale 1–7) subset scores revealed: (1) Useful—Mean 5.69 (SD 0.76); (2) Ease—Mean 5.24 (SD 0.96); (3) Learn—Mean 5.69 (SD 0.74); (4) Satisfy—Mean 5.62 (SD 1.10). Items that received over 80% agreements were: It helps me be more effective; It helps me be more productive; It is useful; It gives me more control over the activities in my life; It makes the things I want to accomplish easier to get done. Participants were highly satisfied and found NegotiAge fun to use (91.7%), with 100% who would recommend it to a friend.
Conclusion
NegotiAge is an Artificial‐Intelligent Caregiver Negotiation Program, that is usable and feasible for family caregivers to become familiar with negotiating conflicts commonly seen in health care.
Background
Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial‐intelligence negotiation training program, NegotiAge, for family caregivers.
Methods
We convened negotiation experts, a geriatrician, a social worker, and community‐based family caregivers. Content matter experts created short videos to teach negotiation skills. Caregivers generated dialogue surrounding conflicts. Computer scientists utilized the dialogue with the Interactive Arbitration Guide Online (IAGO) platform to develop avatar‐based agents (e.g., sibling, older adult, physician) for caregivers to practice negotiating. Pilot testing was conducted with family caregivers to assess usability (USE) and satisfaction (open‐ended questions with thematic analysis).
Results
Development: With NegotiAge, caregivers progress through didactic material, then receive scenarios to negotiate (e.g., physician recommends gastric tube, sibling disagrees with home support, older adult refusing support). Caregivers negotiate in real‐time with avatars who are designed to act like humans, including emotional tactics and irrational behaviors. Caregivers send/receive offers, using tactics until either mutual agreement or time expires. Immediate feedback is generated for the user to improve skills training. Pilot testing: Family caregivers (
n = 12) completed the program and survey. USE questionnaire (Likert scale 1–7) subset scores revealed: (1) Useful—Mean 5.69 (SD 0.76); (2) Ease—Mean 5.24 (SD 0.96); (3) Learn—Mean 5.69 (SD 0.74); (4) Satisfy—Mean 5.62 (SD 1.10). Items that received over 80% agreements were: It helps me be more effective; It helps me be more productive; It is useful; It gives me more control over the activities in my life; It makes the things I want to accomplish easier to get done. Participants were highly satisfied and found NegotiAge fun to use (91.7%), with 100% who would recommend it to a friend.
Conclusion
NegotiAge is an Artificial‐Intelligent Caregiver Negotiation Program, that is usable and feasible for family caregivers to become familiar with negotiating conflicts commonly seen in health care.
Gurney, Nikolos; Pynadath, David V.; Ustun, Volkan
Spontaneous Theory of Mind for Artificial Intelligence Journal Article
In: 2024, (Publisher: [object Object] Version Number: 1).
Abstract | Links | BibTeX | Tags: AI, Social Simulation
@article{gurney_spontaneous_2024,
title = {Spontaneous Theory of Mind for Artificial Intelligence},
author = {Nikolos Gurney and David V. Pynadath and Volkan Ustun},
url = {https://arxiv.org/abs/2402.13272},
doi = {10.48550/ARXIV.2402.13272},
year = {2024},
date = {2024-01-01},
urldate = {2024-03-14},
abstract = {Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM – reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts textbackslashtextitand spontaneously engage in social reasoning.},
note = {Publisher: [object Object]
Version Number: 1},
keywords = {AI, Social Simulation},
pubstate = {published},
tppubtype = {article}
}
Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM – reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts textbackslashtextitand spontaneously engage in social reasoning.
Ustun, Volkan; Jorvekar, Ronit; Gurney, Nikolos; Pynadath, David; Wang, Yunzhe
Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent: Proceedings Article
In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, pp. 313–320, SCITEPRESS - Science and Technology Publications, Rome, Italy, 2024, ISBN: 978-989-758-680-4.
Links | BibTeX | Tags: AI, Cognitive Architecture, Social Simulation
@inproceedings{ustun_assessing_2024,
title = {Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent:},
author = {Volkan Ustun and Ronit Jorvekar and Nikolos Gurney and David Pynadath and Yunzhe Wang},
url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0012388100003636},
doi = {10.5220/0012388100003636},
isbn = {978-989-758-680-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-03-19},
booktitle = {Proceedings of the 16th International Conference on Agents and Artificial Intelligence},
pages = {313–320},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Rome, Italy},
keywords = {AI, Cognitive Architecture, Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
2023
Yang, Daniel; Kommineni, Aditya; Alshehri, Mohammad; Mohanty, Nilamadhab; Modi, Vedant; Gratch, Jonathan; Narayanan, Shrikanth
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models Miscellaneous
2023, (arXiv:2311.03551 [cs]).
Abstract | Links | BibTeX | Tags: AI, UARC, Virtual Humans
@misc{yang_context_2023,
title = {Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models},
author = {Daniel Yang and Aditya Kommineni and Mohammad Alshehri and Nilamadhab Mohanty and Vedant Modi and Jonathan Gratch and Shrikanth Narayanan},
url = {http://arxiv.org/abs/2311.03551},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.},
note = {arXiv:2311.03551 [cs]},
keywords = {AI, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.
Cho, Hyundong; Liu, Shuai; Shi, Taiwei; Jain, Darpan; Rizk, Basem; Huang, Yuyang; Lu, Zixun; Wen, Nuan; Gratch, Jonathan; Ferrara, Emilio; May, Jonathan
Can Language Model Moderators Improve the Health of Online Discourse? Miscellaneous
2023, (arXiv:2311.10781 [cs]).
Abstract | Links | BibTeX | Tags: AI, Dialogue, UARC, Virtual Humans
@misc{cho_can_2023,
title = {Can Language Model Moderators Improve the Health of Online Discourse?},
author = {Hyundong Cho and Shuai Liu and Taiwei Shi and Darpan Jain and Basem Rizk and Yuyang Huang and Zixun Lu and Nuan Wen and Jonathan Gratch and Emilio Ferrara and Jonathan May},
url = {http://arxiv.org/abs/2311.10781},
year = {2023},
date = {2023-11-01},
urldate = {2023-12-07},
publisher = {arXiv},
abstract = {Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.},
note = {arXiv:2311.10781 [cs]},
keywords = {AI, Dialogue, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.
Andrist, Sean; Bohus, Dan; Li, Zongjian; Soleymani, Mohammad
Platform for Situated Intelligence and OpenSense: A Tutorial on Building Multimodal Interactive Applications for Research Proceedings Article
In: International Cconference on Multimodal Interaction, pp. 105–106, ACM, Paris France, 2023, ISBN: 9798400703218.
Links | BibTeX | Tags: AI, UARC, Virtual Humans
@inproceedings{andrist_platform_2023,
title = {Platform for Situated Intelligence and OpenSense: A Tutorial on Building Multimodal Interactive Applications for Research},
author = {Sean Andrist and Dan Bohus and Zongjian Li and Mohammad Soleymani},
url = {https://dl.acm.org/doi/10.1145/3610661.3617603},
doi = {10.1145/3610661.3617603},
isbn = {9798400703218},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
booktitle = {International Cconference on Multimodal Interaction},
pages = {105–106},
publisher = {ACM},
address = {Paris France},
keywords = {AI, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Prinzing, Michael; Garton, Catherine; Berman, Catherine J.; Zhou, Jieni; West, Taylor Nicole; Gratch, Jonathan; Fredrickson, Barbara
Can AI Agents Help Humans to Connect? Technical Report
PsyArXiv 2023.
Abstract | Links | BibTeX | Tags: AI, UARC, Virtual Humans
@techreport{prinzing_can_2023,
title = {Can AI Agents Help Humans to Connect?},
author = {Michael Prinzing and Catherine Garton and Catherine J. Berman and Jieni Zhou and Taylor Nicole West and Jonathan Gratch and Barbara Fredrickson},
url = {https://osf.io/muq6s},
doi = {10.31234/osf.io/muq6s},
year = {2023},
date = {2023-10-01},
urldate = {2023-12-07},
institution = {PsyArXiv},
abstract = {This paper reports on a pre-registered experiment designed to test whether artificial agents can help people to create more moments of high-quality connection with other humans. Of four pre-registered hypotheses, we found (partial) support for only one.},
keywords = {AI, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {techreport}
}
This paper reports on a pre-registered experiment designed to test whether artificial agents can help people to create more moments of high-quality connection with other humans. Of four pre-registered hypotheses, we found (partial) support for only one.
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.
Abstract | Links | BibTeX | Tags: AI, Virtual Humans
@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 = {AI, Virtual Humans},
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.
Gurney, Nikolos; Miller, John H.; Pynadath, David V.
The Role of Heuristics and Biases during Complex Choices with an AI Teammate Journal Article
In: AAAI, vol. 37, no. 5, pp. 5993–6001, 2023, ISSN: 2374-3468, 2159-5399.
Abstract | Links | BibTeX | Tags: AI, Social Simulation, UARC
@article{gurney_role_2023,
title = {The Role of Heuristics and Biases during Complex Choices with an AI Teammate},
author = {Nikolos Gurney and John H. Miller and David V. Pynadath},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/25741},
doi = {10.1609/aaai.v37i5.25741},
issn = {2374-3468, 2159-5399},
year = {2023},
date = {2023-06-01},
urldate = {2023-12-08},
journal = {AAAI},
volume = {37},
number = {5},
pages = {5993–6001},
abstract = {Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating AI helper usage. AI assistance is, arguably, most valuable when humans must make complex choices. We argue that classic experimental methods used to study heuristics and biases are insufficient for studying complex choices made with AI helpers. We adapted an experimental paradigm designed for studying complex choices in such contexts. We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes. The evidence suggests that some participants, particularly those in a loss frame, put too much faith in the AI helper and experienced worse choice outcomes by doing so. The paradigm also generates computational modeling-friendly data allowing future studies of human-AI decision making.},
keywords = {AI, Social Simulation, UARC},
pubstate = {published},
tppubtype = {article}
}
Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating AI helper usage. AI assistance is, arguably, most valuable when humans must make complex choices. We argue that classic experimental methods used to study heuristics and biases are insufficient for studying complex choices made with AI helpers. We adapted an experimental paradigm designed for studying complex choices in such contexts. We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes. The evidence suggests that some participants, particularly those in a loss frame, put too much faith in the AI helper and experienced worse choice outcomes by doing so. The paradigm also generates computational modeling-friendly data allowing future studies of human-AI decision making.
Wu, Haochen; Sequeira, Pedro; Pynadath, David V.
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning Journal Article
In: 2023, (Publisher: arXiv Version Number: 2).
Abstract | Links | BibTeX | Tags: AI, Social Simulation
@article{wu_multiagent_2023,
title = {Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning},
author = {Haochen Wu and Pedro Sequeira and David V. Pynadath},
url = {https://arxiv.org/abs/2302.10238},
doi = {10.48550/ARXIV.2302.10238},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-24},
abstract = {We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.},
note = {Publisher: arXiv
Version Number: 2},
keywords = {AI, Social Simulation},
pubstate = {published},
tppubtype = {article}
}
We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.
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).
Links | BibTeX | Tags: AI, Virtual Humans
@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 = {2023-01-01},
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 = {AI, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
Gurney, Nikolos; Pynadath, David V.; Wang, Ning
Comparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions Book Section
In: vol. 13832, pp. 175–197, 2023, (arXiv:2302.01854 [cs]).
Abstract | Links | BibTeX | Tags: AI, Social Simulation, UARC
@incollection{gurney_comparing_2023,
title = {Comparing Psychometric and Behavioral Predictors of Compliance During Human-AI Interactions},
author = {Nikolos Gurney and David V. Pynadath and Ning Wang},
url = {http://arxiv.org/abs/2302.01854},
doi = {10.1007/978-3-031-30933-5_12},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-15},
volume = {13832},
pages = {175–197},
abstract = {Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differences in people's predisposition to trust can significantly impact their likelihood of complying with recommendations from the AI. Predisposition to trust is often measured with self-report inventories that are administered before interactions. We benchmark a popular measure of this kind against behavioral predictors of compliance. We find that the inventory is a less effective predictor of compliance than the behavioral measures in datasets taken from three previous research projects. This suggests a general property that individual differences in initial behavior are more predictive than differences in self-reported trust attitudes. This result also shows a potential for easily accessible behavioral measures to provide an AI with more accurate models without the use of (often costly) survey instruments.},
note = {arXiv:2302.01854 [cs]},
keywords = {AI, Social Simulation, UARC},
pubstate = {published},
tppubtype = {incollection}
}
Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differences in people's predisposition to trust can significantly impact their likelihood of complying with recommendations from the AI. Predisposition to trust is often measured with self-report inventories that are administered before interactions. We benchmark a popular measure of this kind against behavioral predictors of compliance. We find that the inventory is a less effective predictor of compliance than the behavioral measures in datasets taken from three previous research projects. This suggests a general property that individual differences in initial behavior are more predictive than differences in self-reported trust attitudes. This result also shows a potential for easily accessible behavioral measures to provide an AI with more accurate models without the use of (often costly) survey instruments.
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).
Links | BibTeX | Tags: AI, Virtual Humans
@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 = {2023-01-01},
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 = {AI, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
2022
Gratch, Jonathan; Fast, Nathanael J.
The power to harm: AI assistants pave the way to unethical behavior Journal Article
In: Current Opinion in Psychology, vol. 47, pp. 101382, 2022, ISSN: 2352250X.
Links | BibTeX | Tags: AI, DTIC, Virtual Humans
@article{gratch_power_2022,
title = {The power to harm: AI assistants pave the way to unethical behavior},
author = {Jonathan Gratch and Nathanael J. Fast},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2352250X22001014},
doi = {10.1016/j.copsyc.2022.101382},
issn = {2352250X},
year = {2022},
date = {2022-10-01},
urldate = {2022-09-28},
journal = {Current Opinion in Psychology},
volume = {47},
pages = {101382},
keywords = {AI, DTIC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Hartholt, Arno; Fast, Ed; Leeds, Andrew; Kim, Kevin; Gordon, Andrew; McCullough, Kyle; Ustun, Volkan; Mozgai, Sharon
Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities Proceedings Article
In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp. 1902–1904, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2022, ISBN: 978-1-4503-9213-6.
Abstract | BibTeX | Tags: AI, DTIC, Integration Technology, Machine Learning, UARC, VHTL, Virtual Humans
@inproceedings{hartholt_demonstrating_2022,
title = {Demonstrating the Rapid Integration & Development Environment (RIDE): Embodied Conversational Agent (ECA) and Multiagent Capabilities},
author = {Arno Hartholt and Ed Fast and Andrew Leeds and Kevin Kim and Andrew Gordon and Kyle McCullough and Volkan Ustun and Sharon Mozgai},
isbn = {978-1-4503-9213-6},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-20},
booktitle = {Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems},
pages = {1902–1904},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
series = {AAMAS '22},
abstract = {We demonstrate the Rapid Integration & Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.},
keywords = {AI, DTIC, Integration Technology, Machine Learning, UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
We demonstrate the Rapid Integration & Development Environment (RIDE), a research and development platform that enables rapid prototyping in support of multiagents and embodied conversational agents. RIDE is based on commodity game engines and includes a flexible architecture, system interoperability, and native support for artificial intelligence and machine learning frameworks.
Paun, Silviu; Artstein, Ron; Poesio, Massimo
Statistical Methods for Annotation Analysis Book
Springer International Publishing, Cham, 2022, ISBN: 978-3-031-03753-5 978-3-031-03763-4.
Links | BibTeX | Tags: AI, Natural Language
@book{paun_statistical_2022,
title = {Statistical Methods for Annotation Analysis},
author = {Silviu Paun and Ron Artstein and Massimo Poesio},
url = {https://link.springer.com/10.1007/978-3-031-03763-4},
doi = {10.1007/978-3-031-03763-4},
isbn = {978-3-031-03753-5 978-3-031-03763-4},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-28},
publisher = {Springer International Publishing},
address = {Cham},
keywords = {AI, Natural Language},
pubstate = {published},
tppubtype = {book}
}
Wang, Ning; Greenwald, Eric; Montgomery, Ryan; Leitner, Maxyn
ARIN-561: An Educational Game for Learning Artificial Intelligence for High-School Students Proceedings Article
In: Rodrigo, Maria Mercedes; Matsuda, Noburu; Cristea, Alexandra I.; Dimitrova, Vania (Ed.): Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, pp. 528–531, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-11647-6.
Abstract | Links | BibTeX | Tags: AI, UARC
@inproceedings{wang_arin-561_2022,
title = {ARIN-561: An Educational Game for Learning Artificial Intelligence for High-School Students},
author = {Ning Wang and Eric Greenwald and Ryan Montgomery and Maxyn Leitner},
editor = {Maria Mercedes Rodrigo and Noburu Matsuda and Alexandra I. Cristea and Vania Dimitrova},
url = {https://link.springer.com/chapter/10.1007/978-3-031-11647-6_108},
doi = {10.1007/978-3-031-11647-6_108},
isbn = {978-3-031-11647-6},
year = {2022},
date = {2022-01-01},
booktitle = {Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium},
pages = {528–531},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Artificial Intelligence (AI) is increasingly vital to our future generations, who will join a workforce that utilizes AI-driven tools and contributes to the advancement of AI. Today’s students will need exposure to AI knowledge at a younger age. Relatively little is currently known about how to most effectively provide AI education to K-12 students. In this paper, we discuss the design and evaluation of an educational game for high-school AI education called ARIN-561. Results from pilot studies indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Artificial Intelligence (AI) is increasingly vital to our future generations, who will join a workforce that utilizes AI-driven tools and contributes to the advancement of AI. Today’s students will need exposure to AI knowledge at a younger age. Relatively little is currently known about how to most effectively provide AI education to K-12 students. In this paper, we discuss the design and evaluation of an educational game for high-school AI education called ARIN-561. Results from pilot studies indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.
Gurney, Nikolos; Pynadath, David V.; Wang, Ning
Measuring and Predicting Human Trust in Recommendations from an AI Teammate Proceedings Article
In: Degen, Helmut; Ntoa, Stavroula (Ed.): Artificial Intelligence in HCI, pp. 22–34, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-05643-7.
Abstract | Links | BibTeX | Tags: AI, Social Simulation, UARC
@inproceedings{gurney_measuring_2022,
title = {Measuring and Predicting Human Trust in Recommendations from an AI Teammate},
author = {Nikolos Gurney and David V. Pynadath and Ning Wang},
editor = {Helmut Degen and Stavroula Ntoa},
url = {https://link.springer.com/chapter/10.1007/978-3-031-05643-7_2},
doi = {10.1007/978-3-031-05643-7_2},
isbn = {978-3-031-05643-7},
year = {2022},
date = {2022-01-01},
booktitle = {Artificial Intelligence in HCI},
pages = {22–34},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Predicting compliance with AI recommendations and knowing when to intervene are critical facets of human-AI teaming. AIs are typically deployed in settings where their abilities to evaluate decision variables far exceed the abilities of their human counterparts. However, even though AIs excel at weighing multiple issues and computing near optimal solutions with speed and accuracy beyond that of any human, they still make mistakes. Thus, perfect compliance may be undesirable. This means, just as individuals must know when to follow the advice of other people, it is critical for them to know when to adopt the recommendations from their AI. Well-calibrated trust is thought to be a fundamental aspect of this type of knowledge. We compare the ability of a common trust inventory and the ability of a behavioral measure of trust to predict compliance and success in a reconnaissance mission. We interpret the experimental results to suggest that the behavioral measure is a better predictor of overall mission compliance and success. We discuss how this measure could possibly be used in compliance interventions and related open questions.},
keywords = {AI, Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Predicting compliance with AI recommendations and knowing when to intervene are critical facets of human-AI teaming. AIs are typically deployed in settings where their abilities to evaluate decision variables far exceed the abilities of their human counterparts. However, even though AIs excel at weighing multiple issues and computing near optimal solutions with speed and accuracy beyond that of any human, they still make mistakes. Thus, perfect compliance may be undesirable. This means, just as individuals must know when to follow the advice of other people, it is critical for them to know when to adopt the recommendations from their AI. Well-calibrated trust is thought to be a fundamental aspect of this type of knowledge. We compare the ability of a common trust inventory and the ability of a behavioral measure of trust to predict compliance and success in a reconnaissance mission. We interpret the experimental results to suggest that the behavioral measure is a better predictor of overall mission compliance and success. We discuss how this measure could possibly be used in compliance interventions and related open questions.
Leitner, Maxyn; Greenwald, Eric; Montgomery, Ryan; Wang, Ning
Design and Evaluation of ARIN-561: An Educational Game for Youth Artificial Intelligence Education Proceedings Article
In: Proceedings of the 30th International Conference on Computers in Education, 2022.
Abstract | Links | BibTeX | Tags: AI, UARC
@inproceedings{leitner_design_2022,
title = {Design and Evaluation of ARIN-561: An Educational Game for Youth Artificial Intelligence Education},
author = {Maxyn Leitner and Eric Greenwald and Ryan Montgomery and Ning Wang},
url = {https://par.nsf.gov/servlets/purl/10440195},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the 30th International Conference on Computers in Education},
abstract = {Artificial Intelligence (AI) is increasingly vital to our everyday lives. Future generations will not only consume AI, but work with AI-driven tools and contribute to the development of AI. As such, students will need exposure to AI knowledge at a younger age. Despite this need, relatively little is currently known about how to most effectively provide AI education to K-12 (kindergarten through 12th grade) students. In this paper, we discuss the design of an educational game for high-school AI education called ARIN-561. The game centered around two agents – a player character and a companion robot, as the story and learning experience unfold through conversations between the two agents and explorations that bond the two agents A series of studies were carried out at high schools in the United States to evaluate the efficacy of the game. Results indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Artificial Intelligence (AI) is increasingly vital to our everyday lives. Future generations will not only consume AI, but work with AI-driven tools and contribute to the development of AI. As such, students will need exposure to AI knowledge at a younger age. Despite this need, relatively little is currently known about how to most effectively provide AI education to K-12 (kindergarten through 12th grade) students. In this paper, we discuss the design of an educational game for high-school AI education called ARIN-561. The game centered around two agents – a player character and a companion robot, as the story and learning experience unfold through conversations between the two agents and explorations that bond the two agents A series of studies were carried out at high schools in the United States to evaluate the efficacy of the game. Results indicate the potential of ARIN-561 to build AI knowledge, especially when students spend more time in the game.
2021
Hartholt, Arno; McCullough, Kyle; Fast, Ed; Leeds, Andrew; Mozgai, Sharon; Aris, Tim; Ustun, Volkan; Gordon, Andrew; McGroarty, Christopher
Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE) Proceedings Article
In: 2021.
BibTeX | Tags: AI, DTIC, Integration Technology, Machine Learning, Simulation, UARC, VHTL
@inproceedings{hartholt_rapid_2021,
title = {Rapid Prototyping for Simulation and Training with the Rapid Integration & Development Environment (RIDE)},
author = {Arno Hartholt and Kyle McCullough and Ed Fast and Andrew Leeds and Sharon Mozgai and Tim Aris and Volkan Ustun and Andrew Gordon and Christopher McGroarty},
year = {2021},
date = {2021-11-01},
keywords = {AI, DTIC, Integration Technology, Machine Learning, Simulation, UARC, VHTL},
pubstate = {published},
tppubtype = {inproceedings}
}
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
The Human-Interpreter Problem in Youth Encounters with AI Journal Article
In: Proceedings of the 15th International Conference of the Learning Sciences, pp. 1107–1108, 2021, (Publisher: International Society of the Learning Sciences).
Abstract | Links | BibTeX | Tags: AI, UARC
@article{greenwald_human-interpreter_2021,
title = {The Human-Interpreter Problem in Youth Encounters with AI},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://repository.isls.org//handle/1/7421},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Proceedings of the 15th International Conference of the Learning Sciences},
pages = {1107–1108},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.},
note = {Publisher: International Society of the Learning Sciences},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.
Holder, Eric; Wang, Ning
Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst Journal Article
In: Hum.-Intell. Syst. Integr., vol. 3, no. 2, pp. 139–153, 2021, ISSN: 2524-4884.
Abstract | Links | BibTeX | Tags: AI, UARC
@article{holder_explainable_2021,
title = {Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst},
author = {Eric Holder and Ning Wang},
url = {https://doi.org/10.1007/s42454-020-00021-z},
doi = {10.1007/s42454-020-00021-z},
issn = {2524-4884},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Hum.-Intell. Syst. Integr.},
volume = {3},
number = {2},
pages = {139–153},
abstract = {There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 17, pp. 15526–15533, 2021, ISSN: 2374-3468, (Number: 17).
Abstract | Links | BibTeX | Tags: AI, UARC
@article{greenwald_learning_2021,
title = {Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17828},
doi = {10.1609/aaai.v35i17.17828},
issn = {2374-3468},
year = {2021},
date = {2021-05-01},
urldate = {2023-03-31},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {35},
number = {17},
pages = {15526–15533},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.},
note = {Number: 17},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.
Aryal, Ashrant; Becerik-Gerber, Burcin; Lucas, Gale M.; Roll, Shawn C.
Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation Journal Article
In: IEEE Internet Things J., vol. 8, no. 8, pp. 7089–7100, 2021, ISSN: 2327-4662, 2372-2541.
@article{aryal_intelligent_2021,
title = {Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation},
author = {Ashrant Aryal and Burcin Becerik-Gerber and Gale M. Lucas and Shawn C. Roll},
url = {https://ieeexplore.ieee.org/document/9260148/},
doi = {10.1109/JIOT.2020.3038378},
issn = {2327-4662, 2372-2541},
year = {2021},
date = {2021-04-01},
urldate = {2022-10-24},
journal = {IEEE Internet Things J.},
volume = {8},
number = {8},
pages = {7089–7100},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Wang, Ning; Jajodia, Aditya; Karpurapu, Abhilash; Merchant, Chirag
Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors Proceedings Article
In: Roll, Ido; McNamara, Danielle; Sosnovsky, Sergey; Luckin, Rose; Dimitrova, Vania (Ed.): Artificial Intelligence in Education, pp. 372–377, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-78270-2.
Abstract | Links | BibTeX | Tags: AI, Social Simulation, UARC
@inproceedings{wang_charisma_2021,
title = {Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors},
author = {Ning Wang and Aditya Jajodia and Abhilash Karpurapu and Chirag Merchant},
editor = {Ido Roll and Danielle McNamara and Sergey Sosnovsky and Rose Luckin and Vania Dimitrova},
url = {https://link.springer.com/chapter/10.1007/978-3-030-78270-2_66},
doi = {10.1007/978-3-030-78270-2_66},
isbn = {978-3-030-78270-2},
year = {2021},
date = {2021-01-01},
booktitle = {Artificial Intelligence in Education},
pages = {372–377},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.},
keywords = {AI, Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.