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Predicting human decision-making :from prediction to action /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	658.403
書名/作者:	Predicting human decision-making : : from prediction to action // Ariel Rosenfeld and Sarit Kraus.
作者:	Rosenfeld, Ariel,
其他作者:	Kraus, Sarit,
面頁冊數:	1 PDF (xv, 134 pages) : : illustrations.
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Decision making - Mathematical models.
標題:	Prediction theory.
ISBN:	9781681732756
書目註:	Includes bibliographical references (pages 97-127) and index.
內容註:	1. Introduction -- 1.1 The premise -- 1.2 Prediction tasks taxonomy -- 1.3 Exercises --
摘要、提要註:	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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245 1 0 $a Predicting human decision-making : $b from prediction to action / $c Ariel Rosenfeld and Sarit Kraus.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
300 $a 1 PDF (xv, 134 pages) : $b illustrations.
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490 1 $a Synthesis lectures on artificial intelligence and machine learning, $x 1939-4616 ; $v # 36
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 97-127) and index.
505 0 $a 1. Introduction -- 1.1 The premise -- 1.2 Prediction tasks taxonomy -- 1.3 Exercises --
505 8 $a 2. Utility maximization paradigm -- 2.1 Single decision-maker-decision theory -- 2.1.1 Decision-making under certainty -- 2.1.2 Decision-making under uncertainty -- 2.2 Multiple decision-makers-game theory -- 2.2.1 Normal form games -- 2.2.2 Extensive form games -- 2.3 Are people rational? A short note -- 2.4 Exercises --
505 8 $a 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 --
505 8 $a 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 --
505 8 $a 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 --
505 8 $a 6. Concluding remarks -- Bibliography -- Authors' biographies -- Index.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 3 $a 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
588 $a Title from PDF title page (viewed on January 26, 2018).
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650 0 $a Prediction theory. $3 572217
653 $a intelligent agents
653 $a human decision-making
653 $a prediction models
653 $a human-agent interaction
653 $a decision theory
653 $a game theory
653 $a machine learning
653 $a human factors
653 $a applications
655 0 $a Electronic books. $2 local $3 336502
700 1 $a Kraus, Sarit, $e author. $3 729058
776 0 8 $i Print version: $z 9781681732749 $z 9781681732763
830 0 $a Synthesis digital library of engineering and computer science. $3 461208
830 0 $a Synthesis lectures on artificial intelligence and machine learning ; $v 29 $3 712848
856 4 2 $3 Abstract with links to resource $u http://ieeexplore.ieee.org/servlet/opac?bknumber=8268723