Predicting Human Decision-Making

From Prediction to Action

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Computer Science, Programming
Cover of the book Predicting Human Decision-Making by Ariel Rosenfeld, Sarit Kraus, Ronald Brachman, Peter Stone, Morgan & Claypool Publishers
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Author: Ariel Rosenfeld, Sarit Kraus, Ronald Brachman, Peter Stone ISBN: 9781681733289
Publisher: Morgan & Claypool Publishers Publication: January 22, 2018
Imprint: Morgan & Claypool Publishers Language: English
Author: Ariel Rosenfeld, Sarit Kraus, Ronald Brachman, Peter Stone
ISBN: 9781681733289
Publisher: Morgan & Claypool Publishers
Publication: January 22, 2018
Imprint: Morgan & Claypool Publishers
Language: English

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 as the 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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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 as the 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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