Playing Board Games with the Predict Results of Beam Search Algorithm

TL;DR

This paper presents PROBS, a novel beam search-based algorithm for two-player deterministic board games, which outperforms baselines and operates efficiently with smaller beam sizes compared to traditional MCTS methods.

Contribution

The paper introduces PROBS, a new beam search-based algorithm for game decision-making, offering a simpler alternative to MCTS with competitive performance.

Findings

PROBS outperforms baseline opponents in various board games.

The algorithm remains effective with smaller beam sizes.

PROBS demonstrates increased winning ratios across tested games.

Abstract

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.