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LEARNING OF RESOURCE ALLOCATION STRATEGIES FOR GAME PLAYING
Author(s) -
Markovitch Shaul,
Sella Yaron
Publication year - 1996
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1996.tb00254.x
Subject(s) - computer science , resource allocation , domain (mathematical analysis) , resource (disambiguation) , variation (astronomy) , artificial intelligence , sequential game , machine learning , game theory , microeconomics , economics , mathematics , mathematical analysis , computer network , physics , astrophysics
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.