Learning of Resource Allocation Strategies for game Playing

Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for game Playing. In Proceedings of The Thirteenth International Joint Conference for Artificial Intelligence, 974-979 Chambery, France, 1993.

Abstract

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 get 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.

Keywords: Resource-Bounded Reasoning, Learning in Games

Secondary Keywords:

Online version:

Bibtex entry:

 @inproceedings{Markovitch:1993:LRA,
  Author = {Shaul Markovitch and Yaron Sella},
  Title = {Learning of Resource Allocation Strategies for game Playing},
  Year = {1993},
  Booktitle = {Proceedings of The Thirteenth International Joint Conference for Artificial Intelligence},
  Pages = {974--979},
  Address = {Chambery, France},
  Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Markovitch-Sella-ijcai1993.pdf},
  Keywords = {Resource-Bounded Reasoning, Learning in Games},
  Secondary-keywords = {Anytime Algorithms, Adversary Search, Constructive Induction, Feature Generation, Feature Construction, Decision Trees},
  Abstract = {
    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 get 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.
  }

  }