Self Interested Agent

Self-interested agents, computational entities prioritizing individual gain, pose significant challenges in multi-agent systems requiring cooperation. Current research focuses on designing mechanisms, such as incentive structures, negotiation protocols, and contract-based frameworks, to align these agents' self-interest with broader societal goals or collaborative objectives. Prominent approaches leverage reinforcement learning, game theory, and mechanism design, often employing neural networks for efficient learning and decision-making. This research area is crucial for developing robust and beneficial AI systems, particularly in applications like resource allocation, online advertising, and collaborative robotics, where the coordination of multiple, potentially conflicting, agents is essential.