LEARNING OF RESOURCE ALLOCATION STRATEGIES FOR GAME PLAYING

Human chess players exhibit a large variation in the amount of time they allocate for each move. Yet, the problem of devising resource allocation strategies for game playing has not received enough attention. In this paper we present a framework for studying resource allocation strategies. We define allocation strategy and identify three major types of strategies: static, semi-dynamic, and dynamic. We then describe a method for learning semi-dynamic strategies from self-generated examples. We present an algorithm for assigning classes to the examples based on the utility of investing extra resources. The method was implemented in the domain of checkers, and experimental results show that it is able to learn strategies that improve game-playing performance.