Board Game

Board game research focuses on developing and improving artificial intelligence (AI) agents capable of mastering diverse board games, often without explicit game-specific knowledge. Current research emphasizes efficient algorithms like Monte Carlo Tree Search (MCTS), often enhanced with techniques from combinatorial optimization, and the application of neural network architectures such as transformers for flexible, adaptable game playing across varying board sizes and game rules. This work contributes to both the advancement of AI techniques, particularly in reinforcement learning and general game playing, and a deeper understanding of game strategies and design through AI analysis and the creation of stronger and weaker AI opponents for testing and game balancing.