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Predicting Human Decision-Making [electronic resource] : From Prediction to Action / by Ariel Rosenfeld, Sarit Kraus.

By: Rosenfeld, Ariel [author.].
Contributor(s): Kraus, Sarit [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Artificial Intelligence and Machine Learning: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XVI, 134 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031015786.Subject(s): Artificial intelligence | Machine learning | Neural networks (Computer science)  | Artificial Intelligence | Machine Learning | Mathematical Models of Cognitive Processes and Neural NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Preface -- Acknowledgments -- Introduction -- Utility Maximization Paradigm -- Predicting Human Decision-Making -- From Human Prediction to Intelligent Agents -- Which Model Should I Use? -- Concluding Remarks -- Bibliography -- Authors' Biographies -- Index .
In: Springer Nature eBookSummary: Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures-from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well asthe most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.
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Preface -- Acknowledgments -- Introduction -- Utility Maximization Paradigm -- Predicting Human Decision-Making -- From Human Prediction to Intelligent Agents -- Which Model Should I Use? -- Concluding Remarks -- Bibliography -- Authors' Biographies -- Index .

Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures-from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well asthe most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.

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