Verfügbarkeit: Predicting human decision-making | Technische Universität München

Predicting human decision-making: from prediction to action

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Bibliographische Detailangaben
Beteiligte Personen:	Rosenfeld, Ariel (VerfasserIn), Kraus, Sarit (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	San Rafael Morgan & Claypool Publishers [2018]
Schriftenreihe:	Synthesis lectures on artificial intelligence and machine learning lecture #36
Schlagwörter:	Künstliche Intelligenz Entscheidungsunterstützungssystem Maschinelles Lernen
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030326666&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030326666&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	xv, 134 Seiten Diagramme
ISBN:	9781681732749 9781681732763

Internformat

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Datensatz im Suchindex

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adam_text	IX Preface...................................................................xi Acknowledgments...........................................................xv 1 Introduction..............................................................1 1.1 The Premise..........................................................1 1.2 Prediction Tasks Taxonomy............................................3 1.3 Exercises............................................................5 2 Utility Maximization Paradigm..............................................7 2.1 Single Decision-Maker-Decision Theory................................7 2.1.1 Decision-Making Under Certainty................................8 2.1.2 Decision-Making Under Uncertainty..............................9 2.2 Multiple Decision-Makers-Game Theory................................10 2.2.1 Normal Form Games........................................... 11 2.2.2 Extensive Form Games..........................................14 2.3 Are People Rational? A Short Note ..................................16 2.4 Exercises...........................................................17 3 Predicting Human Decision-Making..........................................21 3.1 Expert-Driven Paradigm..............................................21 3.1.1 Utility Maximization..........................................21 3.1.2 Quantal Response..............................................23 3.1.3 Level-/: .....................................................24 3.1.4 Cognitive Hierarchy...........................................26 3.1.5 Behavioral Sciences...........................................28 3.1.6 Prospect Theory...............................................33 3.1.7 Utilizing Expert-Driven Models................................35 3.2 Data-Driven Paradigm................................................36 3.2.1 Machine Learning: A Human Prediction Perspective..............36 3.2.2 Deep Learning—-Tie Great Redeemer?............................38 3.2.3 Data—The Great Barrier?.......................................40 3.2.4 Additional Aspects in Data Collection............................45 3.2.5 The Data Frontier................................................46 3.2.6 Imbalanced Datasets..............................................47 3.2.7 Levels of Specialization: Who and What to Model..................48 3.2.8 Transfer Learning................................................51 3.3 Hybrid Approach........................................................54 3.3.1 Expert-Driven Features in Machine Learning.......................54 3.3.2 Additional Techniques For Combining Expert-Driven and Data-Driven Models............................................. 55 3.4 Exercises..............................................................56 From Human Prediction to Intelligent Agents..................................61 4.1 Prediction Models in Agent Design......................................61 4.2 Security Games ........................................................63 4.3 Negotiations...........................................................67 4.4 Argumentation..........................................................71 4.5 Voting.................................................................74 4.6 Automotive Industry....................................................77 4.7 Games That People Play.................................................79 4.8 Exercises..............................................................82 Which Model Should I Use?....................................................87 5.1 Is This a Good Prediction Model?.......................................87 5.2 Tie Predicting Human Decision-making (PHD) Flow Graph..................88 5.3 Ethical Considerations.................................................90 5.4 Exercises..............................................................93 Concluding Remarks...........................................................95 Bibliography.................................................................97 Authors’ Biographies........................................................129 Index.......................................................................131 Series Editors: Ronald J. Brachman, Jacobs Technion-CornellInstitute at Cornell Tech Peter Stone, University of Texas at Austin Predicting Human Decision-Making From Prediction to Action Ariel Rosenfeld, Weizmann Institute of Science, Israel Sarit Kraus, Bar-Ilan University, Israel 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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author	Rosenfeld, Ariel Kraus, Sarit
author_GND	(DE-588)1156954770 (DE-588)106709122X
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ctrlnum	(OCoLC)1038748838 (DE-599)BSZ502522976
discipline	Informatik
format	Book
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isbn	9781681732749 9781681732763
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-030326666
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physical	xv, 134 Seiten Diagramme
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series	Synthesis lectures on artificial intelligence and machine learning
series2	Synthesis lectures on artificial intelligence and machine learning
spellingShingle	Rosenfeld, Ariel Kraus, Sarit Predicting human decision-making from prediction to action Synthesis lectures on artificial intelligence and machine learning Künstliche Intelligenz (DE-588)4033447-8 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd
subject_GND	(DE-588)4033447-8 (DE-588)4191815-0 (DE-588)4193754-5
title	Predicting human decision-making from prediction to action
title_auth	Predicting human decision-making from prediction to action
title_exact_search	Predicting human decision-making from prediction to action
title_full	Predicting human decision-making from prediction to action Ariel Rosenfeld, Sarit Kraus
title_fullStr	Predicting human decision-making from prediction to action Ariel Rosenfeld, Sarit Kraus
title_full_unstemmed	Predicting human decision-making from prediction to action Ariel Rosenfeld, Sarit Kraus
title_short	Predicting human decision-making
title_sort	predicting human decision making from prediction to action
title_sub	from prediction to action
topic	Künstliche Intelligenz (DE-588)4033447-8 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd
topic_facet	Künstliche Intelligenz Entscheidungsunterstützungssystem Maschinelles Lernen
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030326666&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030326666&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
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work_keys_str_mv	AT rosenfeldariel predictinghumandecisionmakingfrompredictiontoaction AT kraussarit predictinghumandecisionmakingfrompredictiontoaction

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