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.