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020 _a9783031037672
_9978-3-031-03767-2
024 7 _a10.1007/978-3-031-03767-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aSreedharan, Sarath.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981643
245 1 0 _aExplainable Human-AI Interaction
_h[electronic resource] :
_bA Planning Perspective /
_cby Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXX, 164 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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), 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_981644
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aKulkarni, Anagha.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981645
700 1 _aKambhampati, Subbarao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981646
710 2 _aSpringerLink (Online service)
_981647
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031037771
776 0 8 _iPrinted edition:
_z9783031037573
776 0 8 _iPrinted edition:
_z9783031037870
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_981648
856 4 0 _uhttps://doi.org/10.1007/978-3-031-03767-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c85216
_d85216