Learning of Resource Allocation Strategies for Game Playing

Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for Game Playing. Computational Intelligence, 12:88-105 1996.

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 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.

Keywords: Resource-Bounded Reasoning, Learning in Games, Games

Secondary Keywords:

Online version:

Bibtex entry:

 @article{Markovitch:1996:LRA,
  Author = {Shaul Markovitch and Yaron Sella},
  Title = {Learning of Resource Allocation Strategies for Game Playing},
  Year = {1996},
  Journal = {Computational Intelligence},
  Volume = {12},
  Number = {1},
  Pages = {88-105},
  Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Markovitch-Sella-coin1996.pdf},
  Keywords = {Resource-Bounded Reasoning, Learning in Games, 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 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.
  }

  }