Monte-Carlo Tree Search and Minimax Hybrids with Heuristic Evaluation Functions

Monte-Carlo Tree Search (MCTS) has been found to play suboptimally in some tactical domains due to its highly selective search, focusing only on the most promising moves. In order to combine the strategic strength of MCTS and the tactical strength of minimax, MCTSminimax hybrids have been introduced, embedding shallow minimax searches into the MCTS framework. Their results have been promising even without making use of domain knowledge such as heuristic evaluation functions. This paper continues this line of research for the case where evaluation functions are available. Three different approaches are considered, employing minimax with an evaluation function in the rollout phase of MCTS, as a replacement for the rollout phase, and as a node prior to bias move selection. The latter two approaches are newly proposed. The MCTS-minimax hybrids are tested and compared to their counterparts using evaluation functions without minimax in the domains of Othello, Breakthrough, and Catch the Lion. Introducing minimax in the form of a basic unenhanced alpha-beta search is found to be effective for heuristic node priors in Othello and Catch the Lion, creating the strongest hybrid tested in Othello. The MCTS-minimax hybrids are also found to work well in combination with each other. For their basic implementation in this investigative study, the effective branching factor of a domain is identified as a limiting factor of the hybrid’s performance.