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This paper introduces METAGAMER, the first program designed within the paradigm of Metagame-playing (Metagame). This program plays games in the class of symmetric chess-like games, which includes chess, Chinese chess, checkers, draughts, and Shogi. METAGAMER takes as input the rules of a specific game and analyzes those rules to construct an efficient representation and an evaluation function for that game; they are used by a generic search engine. The strategic analysis performed by METAGAMER relates a set of general knowledge sources to the details of the particular game. Among other properties, this analysis determines the relative value of the different pieces in a given game. Although METAGAMER does not learn from experience, the values resulting from its analysis are qualitatively similar to values used by experts on known games and are sufficient to produce competitive performance the first time METAGAMER plays a new game. Besides being the first Metagame-playing program, this is the first program to have derived useful piece values directly from analysis of the rules of different games. This paper describes the knowledge implemented in METAGAMER, illustrates the piece values METAGAMER derives for chess and checkers, and discusses experiments with METAGAMER on both existing and newly generated games.  相似文献   
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基于Markov对策的多Agent强化学习模型及算法研究   总被引:19,自引:0,他引:19  
在MDP,单Agent可以通过强化学习来寻找问题的最优解。但在多Agent系统中,MDP模型不再适用。同样极小极大Q算法只能解决采用零和对策模型的MAS学习问题。文中采用非零和Markov对策作为多Agent系统学习框架,并提出元对策强化学习的学习模型和元对策Q算法。理论证明元对策Q算法收敛在非零和Markov对策的元对策最优解。  相似文献   
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