LEARNING OF RESOURCE ALLOCATION STRATEGIES FOR GAME PLAYING
作者:
Shaul Markovitch,
Yaron Sella,
期刊:
Computational Intelligence
(WILEY Available online 1996)
卷期:
Volume 12,
issue 1
页码: 88-105
ISSN:0824-7935
年代: 1996
DOI:10.1111/j.1467-8640.1996.tb00254.x
出版商: Blackwell Publishing Ltd
关键词: Resource allocation;learning;class assignment;dynamic classification;game‐playing;time;checkers
数据来源: WILEY
摘要:
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 perf
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