Predicting Human Decision-Making: From Prediction to Action

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. Top Description Table of Contents Author Information 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 Top Description Table of Contents Author Information About the Author(s)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.