Based Potential Game

Based potential games are a framework for modeling multi-agent interactions where the overall system's state converges to a stable equilibrium, even with decentralized decision-making. Current research focuses on applying these games to optimize complex systems, particularly in manufacturing and autonomous vehicle control, using algorithms like Gauss-Seidel methods and gradient-based learning within state-based potential game (SbPG) and its variants (e.g., incorporating Stackelberg strategies or transfer learning). This approach offers a powerful tool for designing self-optimizing systems, improving efficiency and resource allocation in various domains while providing theoretical guarantees of convergence to desirable outcomes.