Cooperative Game

Cooperative game theory explores how multiple agents can collaborate to achieve shared goals, focusing on strategies for efficient resource allocation and coordination, even under incomplete information or communication constraints. Current research emphasizes developing algorithms and models, such as those based on game-theoretic frameworks, reinforcement learning (including self-play and multi-agent RL), and potential games, to improve coordination and cooperation in various settings, including decentralized federated learning and multi-agent systems. These advancements have implications for diverse fields, from improving the efficiency of AI systems and resource management to enhancing the explainability and trustworthiness of AI models through techniques like Shapley value analysis. The development of robust and efficient methods for cooperative game solving is crucial for advancing AI and its applications in complex real-world scenarios.