Playing the game of Hex with theTsetlin Machine and tree search

Abstract

Hex is an abstract mathematical board game where the players aim to build a connection of pieces, traversing the board from edge to edge. The game requires the use of certain patterns to be played at a high level. Artificially Intelligent Hex players have had success using Monte Carlo tree search and current research efforts have introduced neural networks. This thesis looks into the recent Tsetlin Machine pattern-recognition technique, relying on interpretability, in combination with the Monte Carlo tree search method to play the game of Hex. A supervised learning approach has been employed in an effort to teach the Tsetlin Machine beneficial patterns for winning, resulting in around 91% accuracy, 87% recall and 97% precision. Itis demonstrated with a Hex tournament that the Tsetlin Machine is unable to play perfectly on a board of size 6×6 alone, but performs much better in combination with Monte Carlo tree search. Monte Carlo tree search reduced the number of averagely placed piece from around 35.5 down to around 20 and below. The benefit of using the Tsetlin Machine’s interpretable clauses and pattern capabilities are that they can provide valuable knowledge needed for gameplay, and appear helpful for ventures into larger unexplored board sizes. Keywords: Hex, Tsetlin Machine, Monte Carlo tree search, treesearch, board evaluation