Overview
Chess research studies how machines can choose strong moves, model human play, explain strategy, and recognize board positions. Common approaches include tree search and self-play reinforcement learning, neural policies trained on game records, and transformers or language models for move prediction and explanations. Systems are evaluated on game strength, move-prediction accuracy across player ratings, tactical puzzles, and board-recognition benchmarks. A central challenge is balancing strength with human-like behavior and reliable explanations, while testing whether models generalize beyond familiar positions rather than merely matching patterns.
Latest papers
31 papers7 papers in the last four weeks, up 133% on the four weeks before. 0.0% of all new papers.