Predicting Human Decision-Making: From Prediction to Action

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.

Table of 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

About the Authors

Ariel Rosenfeld, Weizmann Institute of Science
Ariel Rosenfeld is a Koshland Postdoctoral Fellow at Weizmann Institute of Science, Israel. He obtained a B.Sc. in Computer Science and Economics, graduating magna cum laude from Tel Aviv University, and a Ph.D. in Computer Science from Bar-Ilan University. Rosenfeld’s research focus is Human-Agent Interaction and, specifically, the prediction of human decision-making. He has published over 20 papers on related topics at top venues such as AAAI, IJCAI, AAMAS, and ECAI conferences and leading journals. Rosenfeld has a rich lecturing and teaching background spanning over a decade and is currently acting as a lecturer at Bar-Ilan University.

Sarit Kraus, Bar-Ilan University
Sarit Kraus is a Professor of Computer Science at Bar-Ilan University, Israel, and an Adjunct Professor at the University of Maryland. She has focused her research on intelligent agents and multi-agent systems. In particular, she developed Diplomat, the first automated agent that negotiated proficiently with people. Kraus has received the EMET Prize for her expertise and contributions to artificial intelligence, the IJCAI “Computers and Thought Award,” the ACM SIGART Agents Research award, and the prestigious Advanced ERC Grant. She also received a special commendation from the city of Los Angeles, together with Professor Tambe, Professor Ordonez, and their students, for the creation of the ARMOR security scheduling system. Kraus has published over 300 papers in leading journals and major conferences.