Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Fast Planning in Stochastic Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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There has been growing interest in AI and related disciplines in the emerging field of computational game theory. This area revisits the problems and solutions of classical game theory with an explicit emphasis on computational efficiency and scalability. The interest from the AI community arises from several sources, including models and algorithms for multi-agent systems, design of electronic commerce agents, and the study of compact representations for complex environments that permit efficient learning and planning algorithms.In the talk, I will survey some recent results in computational game theory, and highlight similarities with algorithms, representations and motivation in the AI and machine learning literature. The topics examined will include a simple study of gradient algorithms in general games [1], the application of reinforcement learning algorithms and their generalizations to stochastic games [2], and the introduction of compact graphical models for multi-player games [3,4]. Interesting directions for further work will be discussed.