Playing Board Games with the Predict Results of Beam Search Algorithm
This addresses game-playing AI for board games, but it is incremental as it adapts an existing search method.
The paper tackles decision-making in two-player deterministic games by introducing the PROBS algorithm, which uses beam search instead of Monte Carlo Tree Search, and shows it achieves an increased winning ratio against baselines, even with small beam sizes.
This paper introduces a novel algorithm for two-player deterministic games with perfect information, which we call PROBS (Predict Results of Beam Search). Unlike existing methods that predominantly rely on Monte Carlo Tree Search (MCTS) for decision processes, our approach leverages a simpler beam search algorithm. We evaluate the performance of our algorithm across a selection of board games, where it consistently demonstrates an increased winning ratio against baseline opponents. A key result of this study is that the PROBS algorithm operates effectively, even when the beam search size is considerably smaller than the average number of turns in the game.