Isbn: 9783031037573 - explainable human-ai interaction: a planning perspective (13 risultati)

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    Editore: Springer 2022-01, 2022

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    Condizione: New. pp. 164 1st edition NO-PA16APR2015-KAP.

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    Editore: Springer, 2022

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  • Lingua: Inglese

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    Taschenbuch. Condizione: Neu. Explainable Human-AI Interaction | A Planning Perspective | Sarath Sreedharan (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xx | Englisch | 2022 | Springer | EAN 9783031037573 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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    Editore: Springer, 2022

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  • Lingua: Inglese

    Editore: Springer International Publishing Jan 2022, 2022

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -From 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. 188 pp. Englisch.…

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    Editore: Springer, 2022

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    Condizione: New. Print on Demand pp. 164.

  • Lingua: Inglese

    Editore: Springer, 2022

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    Condizione: New. PRINT ON DEMAND pp. 164.

  • Lingua: Inglese

    Editore: Palgrave Macmillan, 2022

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    Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - From 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.…

  • Lingua: Inglese

    Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2022

    303103757X / 9783031037573

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    Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. From 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 f.…

  • Lingua: Inglese

    Editore: Springer, Springer Jan 2022, 2022

    303103757X / 9783031037573

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    Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -From 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 188 pp. Englisch.…