Evaluation Function Based Monte-Carlo LOA

Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantially. In the game of Lines of Action (LOA), which has been dominated in the past by αβ, MCTS is making an inroad. In this paper we investigate how to use a positional evaluation function in a Monte-Carlo simulation-based LOA program (MC-LOA). Four different simulation strategies are designed, called Evaluation CutOff, Corrective, Greedy, and Mixed, which use an evaluation function in several ways. Experimental results reveal that the Mixed strategy is the best among them. This strategy draws the moves randomly based on their transition probabilities in the first part of a simulation, but selects them based on their evaluation score in the second part of a simulation. Using this simulation strategy the MC-LOA program plays at the same level as the αβ program MIA, the best LOA playing entity in the world.