Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
A generalised quiescence search algorithm
Artificial Intelligence - Special issue on computer chess
Singular extensions: adding selectivity to brute-force searching
Artificial Intelligence - Special issue on computer chess
How computers play chess
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Information Filtering: Selection Mechanisms in Learning Systems
Machine Learning
Machine Learning
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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 did not receive 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 proceed to describe a method for learning semi-dynamic strategies from self generated examples. The method assigns 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.