In recent years, game theory has emerged as a powerful framework for analyzing strategic interactions in complex systems, and its application has expanded significantly in the context of artificial intelligence (AI) and large models. AI agents, particularly those powered by advanced machine learning algorithms and large-scale models, are now capable of engaging in highly sophisticated decision-making processes that mimic real-world strategic scenarios.

This workshop aims to explore the intersection of game theory and AI, focusing on how game-theoretic concepts can be applied to the design, analysis, and behavior of AI agents. We will delve into topics such as multi-agent systems, reinforcement learning, and mechanism design, with a special emphasis on large models like deep neural networks and generative models.

As AI agents increasingly interact with each other in various settings—ranging from cooperative team dynamics to competitive environments—the insights provided by game theory are crucial for understanding and predicting their behavior. Additionally, the emergence of large language models (LLMs) and other complex AI architectures has introduced new challenges and opportunities for modeling strategic decision-making at scale.

The goal of this workshop is to foster a deeper understanding of these cutting-edge developments, highlighting both theoretical foundations and practical applications in AI, and to discuss the ethical and societal implications of deploying AI systems in strategic environments.