Zero Sum Game

Zero-sum games, where one player's gain is exactly balanced by another's loss, are a fundamental concept in game theory with applications ranging from adversarial machine learning to multi-agent systems. Current research focuses on developing efficient algorithms, such as variations of multiplicative weight updates and extra-gradient methods, to find Nash equilibria (optimal strategies) in increasingly complex settings, including those with periodic changes, constraints, and memory asymmetry. These advancements are crucial for improving the robustness and performance of AI agents in competitive environments and for understanding strategic interactions in various real-world scenarios, from negotiation to resource allocation.