Decision Forests for Computer Go Feature Learning
Author: Francois van Niekerk
Supervisor: Dr. Steve Kroon
Thesis for MSc (Computer Science) at Stellenbosch University
In computer Go, moves are typically selected with the aid of a tree search algorithm. Monte-Carlo tree search (MCTS) is currently the dominant algorithm in computer Go. It has been shown that the inclusion of domain knowledge in MCTS is able to vastly improve the strength of MCTS engines. A successful approach to representing domain knowledge in computer Go is the use of appropriately weighted tactical features and pattern features, which are comprised of a number of hand-crafted heuristics and a collection of patterns respectively. However, tactical features are hand-crafted specifically for Go, and pattern features are Go-specific, making it unclear how they can be easily transferred to other domains.
As such, this work proposes a new approach to representing domain knowledge, decision tree features. These features evaluate a state-action pair by descending a decision tree, with queries recursively partitioning the state-action pair input space, and returning a weight corresponding to the partition element represented by the resultant leaf node. In this work, decision tree features are applied to computer Go, in order to determine their feasibility in comparison to state-of-the-art use of tactical and pattern features. In this application of decision tree features, each query in the decision tree descent path refines information about the board position surrounding a candidate move.
The results of this work showed that a feature instance with decision tree features is a feasible alternative to the state-of-the-art use of tactical and pattern features in computer Go, in terms of move prediction and playing strength, even though computer Go is a relatively well-developed research area. A move prediction rate of 35.9% was achieved with tactical and decision tree features, and they showed comparable performance to the state of the art when integrated into an MCTS engine with progressive widening.
We conclude that the decision tree feature approach shows potential as a method for automatically extracting domain knowledge in new domains. These features can be used to evaluate state-action pairs for guiding search-based techniques, such as MCTS, or for action-prediction tasks.