Non Cooperative

Non-cooperative multi-agent systems research focuses on understanding and managing interactions between agents with potentially conflicting goals or behaviors. Current work explores strategies for mitigating adversarial actions, such as employing theory-of-mind models to identify deceptive communication or developing trust metrics to differentiate cooperative from non-cooperative agents, often within reinforcement learning frameworks. These advancements are crucial for improving the robustness and efficiency of multi-agent systems in diverse applications, including autonomous driving, human-computer interaction, and creative tasks like poetry generation, where diverse outputs are desired. The ultimate aim is to design systems that can effectively function and achieve objectives even in the presence of unpredictable or antagonistic agents.