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.

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 decisionmaking. 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.