From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humansswinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic humanAI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), andbe ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.

Autorentext

Sarath Sreedharan is a Ph.D. student at Arizona State University working with Prof. Subbarao Kambhampati. His primary research interests lie in the area of human-aware and explainable AI, with a focus on sequential decision-making problems. Sarath's research has been featured in various premier research conferences, including IJCAI, AAAI, AAMAS,ICAPS, ICRA, IROS, etc., and journals like AIJ. He was also the recipient of Outstanding Program Committee Member Award at AAAI-2020.Anagha Kulkarni is an AI Research Scientist at Invitae. Before that, she received her Ph.D. in Computer Science from Arizona State University. Her Ph.D. thesis was in the area of human-aware AI and automated planning. Anagha's research has been featured in various premier conferences like AAAI,IJCAI, ICAPS, AAMAS, ICRA, and IROS.Subbarao Kambhampati is a professor in the School of Computing & AI at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems. He is a fellow of the Association for the Advancement of Artificial Intelligence, the American Association for the Advancement of Science, and the Association for Computing Machinery, and was an NSF Young Investigator. He was the president of the Association for the Advancement of Artificial Intelligence, trustee of the International Joint Conference on Artificial Intelligence, and a founding board member of Partnership on AI. Kambhampati's research, as well as his views on the progress and societal impacts of AI, have been featured in multiple national and international media outlets.

Inhalt
Preface.- Acknowledgments.- Introduction.- Measures of Interpretability.- Explicable Behavior Generation.- Legible Behavior.- Explanation as Model Reconciliation.- Acquiring Mental Models for Explanations.- Balancing Communication and Behavior.- Explaining in the Presence of Vocabulary Mismatch.- Obfuscatory Behavior and Deceptive Communication.- Applications.- Conclusion.- Bibliography.- Authors' Biographies.- Index.