Monte carlo tree search with heuristic evaluations using implicit minimax backups

Marc Lanctot, Mark H M Winands, Tom Pepels, Nathan R. Sturtevant

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Monte Carlo Tree Search (MCTS) has improved the performance of game engines in domains such as Go, Hex, and general game playing. MCTS has been shown to outperform classic αβ search in games where good heuristic evaluations are difficult to obtain. In recent years, combining ideas from traditional minimax search in MCTS has been shown to be advantageous in some domains, such as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new way to use heuristic evaluations to guide the MCTS search by storing the two sources of information, estimated win rates and heuristic evaluations, separately. Rather than using the heuristic evaluations to replace the playouts, our technique backs them up implicitly during the MCTS simulations. These minimax values are then used to guide future simulations. We show that using implicit minimax backups leads to stronger play performance in Kalah, Breakthrough, and Lines of Action.
Original languageEnglish
Title of host publicationIEEE Conference on Computatonal Intelligence and Games, CIG
PublisherIEEE Computer Society
ISBN (Print)9781479935468
Publication statusPublished - 21 Oct 2014

Cite this