A framework for data mining on combinatorial game theory

Article type: Research Article

Authors: Hooks, David | Ding, Qin^{; *}

Affiliations: Department of Computer Science, East Carolina University, Greenville, NC 27858, USA

Correspondence: [*] Corresponding author. Tel.: +1 252 328 9686; Fax: +1 252 328 0715; E-mail: dingq@ecu.edu.

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.

Keywords: Data mining, Bayesian classification, combinatorial game theory, Audacity game

DOI: 10.3233/JCM-2009-0238

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 9, no. s1, pp. S91-S98, 2009