  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.
