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国際標準書誌記述(ISBD)
Predicting human decision-making :fr...
~
Kraus, Sarit,
Predicting human decision-making :from prediction to action /
レコード種別:
コンピュータ・メディア : 単行資料
[NT 15000414] null:
658.403
タイトル / 著者:
Predicting human decision-making : : from prediction to action // Ariel Rosenfeld and Sarit Kraus.
著者:
Rosenfeld, Ariel,
その他の著者:
Kraus, Sarit,
記述:
1 PDF (xv, 134 pages) : : illustrations.
注記:
Part of: Synthesis digital library of engineering and computer science.
主題:
Decision making - Mathematical models.
主題:
Prediction theory.
国際標準図書番号 (ISBN) :
9781681732756
[NT 15000227] null:
Includes bibliographical references (pages 97-127) and index.
[NT 15000228] null:
1. Introduction -- 1.1 The premise -- 1.2 Prediction tasks taxonomy -- 1.3 Exercises --
[NT 15000229] null:
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.
電子資源:
http://ieeexplore.ieee.org/servlet/opac?bknumber=8268723
Predicting human decision-making :from prediction to action /
Rosenfeld, Ariel,
Predicting human decision-making :
from prediction to action /Ariel Rosenfeld and Sarit Kraus. - 1 PDF (xv, 134 pages) :illustrations. - Synthesis lectures on artificial intelligence and machine learning,# 361939-4616 ;. - Synthesis digital library of engineering and computer science..
Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 97-127) and index.
1. Introduction -- 1.1 The premise -- 1.2 Prediction tasks taxonomy -- 1.3 Exercises --
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Compendex
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.
Mode of access: World Wide Web.
ISBN: 9781681732756
Standard No.: 10.2200/S00820ED1V01Y201712AIM036doiSubjects--Topical Terms:
337886
Decision making
--Mathematical models.Subjects--Index Terms:
intelligent agentsIndex Terms--Genre/Form:
336502
Electronic books.
LC Class. No.: HD30.23 / .R676 2018
Dewey Class. No.: 658.403
Predicting human decision-making :from prediction to action /
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3. Predicting human decision-making -- 3.1 Expert-driven paradigm -- 3.1.1 Utility maximization -- 3.1.2 Quantal response -- 3.1.3 Level-k -- 3.1.4 Cognitive hierarchy -- 3.1.5 Behavioral sciences -- 3.1.6 Prospect theory -- 3.1.7 Utilizing expert-driven models -- 3.2 Data-driven paradigm -- 3.2.1 Machine learning: a human prediction perspective -- 3.2.2 Deep learning, the great redeemer? -- 3.2.3 Data, the great barrier? -- 3.2.4 Additional aspects in data collection -- 3.2.5 The data frontier -- 3.2.6 Imbalanced datasets -- 3.2.7 Levels of specialization: who and what to model -- 3.2.8 Transfer learning -- 3.3 Hybrid approach -- 3.3.1 Expert-driven features in machine learning -- 3.3.2 Additional techniques for combining expert-driven and data-driven models -- 3.4 Exercises --
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4. From human prediction to intelligent agents -- 4.1 Prediction models in agent design -- 4.2 Security games -- 4.3 Negotiations -- 4.4 Argumentation -- 4.5 Voting -- 4.6 Automotive industry -- 4.7 Games that people play -- 4.8 Exercises --
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5. Which model should I use? -- 5.1 Is this a good prediction model? -- 5.2 The predicting human decision-making (PHD) flow graph -- 5.3 Ethical considerations -- 5.4 Exercises --
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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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マルチメディア (複合媒体資料)
マルチメディアファイル
http://ieeexplore.ieee.org/servlet/opac?bknumber=8268723
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