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On similarities between inference in game theory and machine learning
Journal of Artificial Intelligence Research
Concurrent modeling of alternative worlds with polyagents
MABS'06 Proceedings of the 2006 international conference on Multi-agent-based simulation VII
A neighborhood correlated empirical weighted algorithm for fictitious play
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
An efficient optimization approach to real-time coordinated and integrated freeway traffic control
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Sampled fictitious play for approximate dynamic programming
Computers and Operations Research
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A sampled fictitious play based learning algorithm for infinite horizon Markov decision processes
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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In this paper, we investigate the properties of the sampled version of the fictitious play algorithm, familiar from game theory, for games with identical payoffs, and propose a heuristic based on fictitious play as a solution procedure for discrete optimization problems of the form max{ u( y):y = ( y1,..., y n ) ?Y1Yn }, i.e., in which the feasible region is a Cartesian product of finite setsYi,i ?N = {1,..., n}. The contributions of this paper are twofold. In the first part of the paper, we broaden the existing results on convergence properties of the fictitious play algorithm on games with identical payoffs to include an approximate fictitious play algorithm that allows for errors in players' best replies. Moreover, we introduce sampling-based approximate fictitious play that possesses the above convergence properties, and at the same time provides a computationally efficient method for implementing fictitious play. In the second part of the paper, we motivate the use of algorithms based on sampled fictitious play to solve optimization problems in the above form with particular focus on the problems in which the objective functionu(脗·) comes from a "black box," such as a simulation model, where significant computational effort is required for each function evaluation.