Chess Engine

Chess engines, aiming to replicate and surpass human chess mastery, are a significant area of artificial intelligence research. Current research focuses on developing high-performance engines using deep learning architectures like transformers and convolutional neural networks, often combined with search algorithms such as Monte Carlo Tree Search (MCTS) or novel approaches that minimize or eliminate search entirely. These advancements are pushing the boundaries of AI capabilities in complex decision-making, leading to insights into both efficient computation and the nature of human-like intelligence in game playing. Furthermore, research is exploring methods to improve the interpretability of these complex models and to design engines that can effectively collaborate with players of varying skill levels.