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Decentralized Learning of Nash Equilibria in Multi-Person Stochastic Games With Incomplete Information

Sastry, PS and Phansalkar, VV and Thathachar, MAL (1994) Decentralized Learning of Nash Equilibria in Multi-Person Stochastic Games With Incomplete Information. In: IEEE Transactions on Systems, Man and Cybernetics, 24 (5). pp. 769-777.

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Abstract

A multi-person discrete game where the payoff after each play is stochastic is considered. The distribution of the random payoff is unknown to the players and further none of the players know the strategies or the actual moves of other players. A learning algorithm for the game based on a decentralized team of learning automata is presented. It is proved that all stable stationary points of the algorithm are Nash equilibria for the game. Two special cases of the game are also discussed, namely, game with common pay off and the relaxation labelling problem. The former has applications such as pattern recognition and the latter is a problem widely studied in computer vision. For the two special cases it is shown that the algorithm always converges to a desirable solution. India ' s

Item Type: Journal Article
Additional Information: Copyright 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 25 Aug 2008
Last Modified: 19 Sep 2010 04:27
URI: http://eprints.iisc.ernet.in/id/eprint/6989

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