A framework for data mining on combinatorial game theory

  • Authors:
  • David Hooks;Qin Ding

  • Affiliations:
  • Department of Computer Science, East Carolina University, Greenville, NC 27858, USA;(Correspd. Tel.: +1 252 328 9686/ Fax: +1 252 328 0715/ E-mail: dingq@ecu.edu) Department of Computer Science, East Carolina University, Greenville, NC 27858, USA

  • Venue:
  • Journal of Computational Methods in Sciences and Engineering
  • Year:
  • 2009

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Abstract

Combinatorics is the study of discrete, finite spaces. Combinatorial games are games that can be studied through the use of combinatorics. They are typically, but not necessarily, two-player games with a finite set of possible states and a well-defined winning condition. The search space in combinatorial games is typically very large. In this paper, we proposed a framework to apply data mining techniques such as Bayesian classification to the combinatorial game theory, in particular, a game called "Audacity". Our experimental results show that the Bayesian classification is effective for discovering classification rules in combinatorial games, such as the "Audacity" game.