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タイトルImproving Simulated Annealing by Replacing Its Variables with Game-Theoretic Utility Maximizers
本文(外部サイト)http://hdl.handle.net/2060/20010106095
著者(英)Tumer, Kagan; Bandari, Esfandiar; Wolpert, David H.
著者所属(英)NASA Ames Research Center
発行日2001-09-01
言語eng
内容記述The game-theory field of Collective INtelligence (COIN) concerns the design of computer-based players engaged in a non-cooperative game so that as those players pursue their self-interests, a pre-specified global goal for the collective computational system is achieved as a side-effect. Previous implementations of COIN algorithms have outperformed conventional techniques by up to several orders of magnitude, on domains ranging from telecommunications control to optimization in congestion problems. Recent mathematical developments have revealed that these previously developed algorithms were based on only two of the three factors determining performance. Consideration of only the third factor would instead lead to conventional optimization techniques like simulated annealing that have little to do with non-cooperative games. In this paper we present an algorithm based on all three terms at once. This algorithm can be viewed as a way to modify simulated annealing by recasting it as a non-cooperative game, with each variable replaced by a player. This recasting allows us to leverage the intelligent behavior of the individual players to substantially improve the exploration step of the simulated annealing. Experiments are presented demonstrating that this recasting significantly improves simulated annealing for a model of an economic process run over an underlying small-worlds topology. Furthermore, these experiments reveal novel small-worlds phenomena, and highlight the shortcomings of conventional mechanism design in bounded rationality domains.
NASA分類Mathematical and Computer Sciences (General)
レポートNONAS 1.15:210930
NASA/TM-2001-210930
権利No Copyright
URIhttps://repository.exst.jaxa.jp/dspace/handle/a-is/92402


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