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Artificial Intelligence for General Game Playing

From the Stanford MediaX Interactive Media & Games Seminar Series; Michael Genesereth, Associate Professor of Computer Science at Stanford University, examines the challenges of general game playing and techniques for meeting those challenges. He also describes the annual GGP competition; and talks about the practical and theoretical value of work in this area.

A GENERAL GAME PLAYING (GGP) SYSTEM is one that can play arbitrary games based solely on formal game descriptions supplied at “runtime”. (Translation: it does not get the rules until the game starts.) Unlike specialized game players, such as Deep Blue, general game players do not rely on algorithms designed in advance by their programmers for a specific game; instead, they utilize general technologies appropriate to any (discrete) game. In this brief talk, Dr. Genesereth summarizes the challenges of general game playing and techniques for meeting those challenges; he describes the annual GGP competition; and he talks about the practical and theoretical value of work in this area.

Michael Genesereth is an associate professor in the Computer Science Department at Stanford University. He received his Sc.B. in Physics from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. Dr. Genesereth is most known for his work on Computational Logic and applications of that work in Enterprise Management, Electronic Commerce, and Computational Law. He is one of the founders of Teknowledge, CommerceNet, and Mergent Systems. Genesereth is the current director of the Logic Group at Stanford and research director of CodeX(the Stanford Center for Computers and Law). Publications include Logical Foundations of Artificial Intelligence and Data Integration – The Relational Logic Approach.