Authors: F. van Niekerk and S. Kroon
Published at the Workshop on Computer Games at IJCAI’13.
Abstract
Monte-Carlo Tree Search (MCTS) is currently the dominant algorithm in Computer Go. MCTS is an asymmetric tree search technique employing stochastic simulations to evaluate leaves and guide the search. Using features to further guide MCTS is a powerful approach to improving performance. In Computer Go, these features are typically comprised of a number of hand-crafted heuristics and a collection of patterns, with weights for these features usually trained using data from high-level Go games. This paper investigates the feasibility of using decision trees to generate features for Computer Go. Our experiments show that while this approach exhibits potential, our initial prototype is not as powerful as using traditional pattern features.