Strategy Game

Strategy game research focuses on developing and evaluating AI agents capable of mastering complex strategic interactions, aiming to improve game design, testing, and player experience. Current research emphasizes the application of reinforcement learning, Monte Carlo Tree Search, and large language models (LLMs), often combined with techniques like state abstraction and graph neural networks, to create more sophisticated and adaptable agents. These advancements have implications for game development (e.g., automated difficulty balancing and testing), AI research (e.g., multi-agent systems and strategic reasoning), and even other fields like public health (e.g., optimizing resource allocation). The development of robust and generalizable AI agents for strategy games continues to push the boundaries of artificial intelligence and game theory.