Learning to play the chess variant Crazyhouse with deep learning and domain knowledgeMakovec, Anei (Avtor) Guid, Matej (Mentor) Pirker, Johanna (Komentor) Crazyhousechess variantsneural networksdomain knowledgeBest-Change ratesMonte Carlo tree searchIn the evolving landscape of game-playing algorithms, Crazyhouse's reintroduction of captured pieces presents a unique challenge that distinguishes it from traditional chess. In this thesis, we explore a hybrid approach that combines domain knowledge with neural network-based evaluations, aiming for an optimal balance of performance. Through rigorous experiments, including self-play, matchups against a variant of the known program, Go-deep experiments, and move score deviations, we present compelling evidence for the effectiveness of a weighted sum of evaluations from a traditional evaluation function and an AlphaZero-style neural network. Remarkably, in our experiments, the combination of 75% neural network and 25% traditional evaluation consistently emerged as the most effective choice. Furthermore, we introduce the use of Best-Change rates, previously associated with evaluation quality, in the context of Monte Carlo tree search-based algorithms. Our approach may hold promise beyond Crazyhouse, especially in domains where established heuristic knowledge has proven effective. Additionally, it provides a basis for potentially clarifying chessboard decisions - a significant departure from the complexity of neural network decision-making. The classical evaluation function provides interpretable domain knowledge, offering a potential avenue for understandable decision-making.20242024-03-12 12:05:00Magistrsko delo/naloga154972VisID: 36460COBISS_ID: 187474947sl