Comparison of Bayesian move prediction systems for Computer Go

Abstract

Abstract-Since the early days of research on Computer Go, move prediction systems are an important building block for Go playing programs. Only recently, with the rise of Monte Carlo Tree Search (MCTS) algorithms, the strength of Computer Go programs increased immensely while move prediction remains to be an integral part of state of the art programs. In this paper we review three Bayesian move prediction systems that have been published in recent years and empirically compare them under equal conditions. Our experiments reveal that, given identical input data, the three systems can achieve almost identical prediction rates while differing substantially in their needs for computational and memory resources. From the analysis of our results, we are able to further improve the prediction rates for all three systems.

Move Prediction in Go – Modelling Feature Interactions Using Latent Factors

“…Most move predictors for Go are either using Neural Networks [6,7] or are estimating ratings for moves using the Bradley Terry (BT) model or related models [3,4,8]. Latter mentioned approaches model each move decision as a competition between players, the move chosen by the human expert player is then the winning player and its value is updated accordingly.…”

“…Another possibility to divide the Go move predictors into two classes is how they consider interactions between features. There are two variants, one models the full-interaction of all features [3,9] and the others do not consider them at all [4,6,8].…”

“…Stern's [3] full-interaction model used a learning set of 181,000 games with 4 feature groups but only predicted 34% of the moves correctly. Using the same approach with no interaction, Wistuba et al [4] has shown that easily 9 feature groups can be used and, using a learning set of only 10,000 games, 37% of moves were predicted correctly. Ibidem, it was tried to combine advantages of both approaches by using an approach without feature interaction and adding a special feature that represented a combination of few features.…”

“…This work will use the terminology introduced in [4]. A single game in Go is formalized as a tuple G := (S, A, Γ, δ) where S := C n×n is the set of possible states and C := {black, white, empty} is the set of colors.…”

“…Upfront, the strength of each feature is learned with various supervised learning algorithms. The prediction can be improved by using additional features, but as seen in [3,4] it can also be improved by considering the impact of feature interactions.…”