KEYNOTE #3 [PRIMA-2012]

Empirical Game-Theoretic Analysis for Practical Strategic Reasoning

Michael Wellman
University of Michigan, USA

Keynote Abstract: The games agents play--in markets, conflicts, or most other contexts--often defy strict game-theoretic analysis. Games may be unmanageably large (combinatorial or infinite state or action spaces), and present severely imperfect information, which could be further complicated by partial dynamic revelation. Moreover, the game may be specified procedurally, for instance by a simulator, rather than in an explicit game form. With colleagues and students over the past few years, I have been developing a body of techniques for strategic analysis, adopting the game-theoretic framework but employing it in domains where direct "model-and-solve" cannot apply. This empirical game-theoretic methodology embraces simulation, approximation, statistics and learning, and search. Through applications to canonical auction games, and other rich multiagent scenarios, we demonstrate the value of empirical methods for extending the scope of game-theoretic analysis.

Bio-data: Michael P. Wellman is Professor of Computer Science & Engineering at the University of Michigan, where he leads the Strategic Reasoning Group in the Artificial Intelligence Laboratory. He received a PhD from the Massachusetts Institute of Technology in 1988 for his work in qualitative probabilistic reasoning and decision-theoretic planning. For over twenty years, Wellman has worked at the intersection of Computer Science and Economics, in the process pioneering approaches to computational markets and trading agent design and analysis. Market designs and infrastructure developed by his research group have been influential in the academic community as well as in electronic commerce practice. Wellman is a Fellow of the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.

http://ktw.mimos.my/prima2012/sp...