A framework for data mining on combinatorial game theory
David Hooks, Qin Ding · Journal of Computational Methods in Sciences and Engineering · 2009
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 combina torial 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.