Multi-Agent Altruism

Topic

Multi-agent altruism is a framework in artificial intelligence and distributed systems where individual agents are designed or trained to incur personal costs to benefit other agents or the collective system. Drawing inspiration from biological altruism and evolutionary game theory, it utilizes mechanisms like Hamilton's rule and decentralized reward structures to improve coordination, resolve conflicts, and maximize overall team efficiency in complex environments. This approach is widely applied in cooperative multi-agent reinforcement learning, autonomous vehicle navigation, and modular robotics.