Playing Board Games with the Predict Results of Beam Search Algorithm

In the domain of artificial intelligence, two-player board games have historically served as pivotal'toy problems' for exploring and advancing search and planning algorithms within vast decision spaces. The outstanding algorithm AlphaZero (Silver et al. [2016] Silver et al. [2017a] Silver et al. [2017b]) achieved superhuman performance in the game of Go, chess, and other board games without the use of human expertise in these games. In this work, we introduce a new approach to solving such games. The main idea is that the algorithm iterates through possible moves using beam search, and then learns to predict the outcome of this search. This concept gives rise to the name of the algorithm, PROBS - Predict Results of Beam Search. This approach shows promising results -- it demonstrates an increase in the winning percentage during the training process and shows improvement with the use of greater computational power. Although this new approach to solving board games does not improve upon state-of-the-art approaches, it demonstrates a new working concept that may inspire researchers to develop new methods in other areas. The foundation of the PROBS algorithm is the iterative training of two neural networks. The first network is a value function, V (s), which predicts the expected utility from the current state.