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008			220601s2022 sz | o |||| 0|eng d
020			_a9783031037672
024	7		_a10.1007/978-3-031-03767-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .H85 _b2022 EB
100	1		_aSreedharan, Sarath _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687666
245	1	0	_aExplainable Human-AI Interaction : _bA Planning Perspective _cby Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XX, 164 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aPreface -- 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.
520			_aFrom its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans-swinging 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 human‒AI 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), and be 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.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9155848 _aInteracción hombre-ordenador
700	1		_aKulkarni, Anagha _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687667
700	1		_aKambhampati, Subbarao _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687668
776	0	8	_iPrinted edition: _z9783031037771
776	0	8	_iPrinted edition: _z9783031037573
776	0	8	_iPrinted edition: _z9783031037870
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-03767-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eb _zSI