Multiagents

Multiagents, or multi-agent systems (MAS), describe collections of autonomous agents that interact within an environment to achieve individual goals or a shared objective. An agent is a software or hardware entity that perceives its environment, makes decisions, and acts. MAS studies how agents collaborate, compete, and coordinate to accomplish tasks that are difficult for a single agent to achieve alone.

Agents possess autonomy, social ability to communicate or negotiate, reactivity to changing conditions, and proactive goal-directed behavior. MAS architectures range from centralized control to fully decentralized systems built on local interactions. Agent models include deliberative, reactive, and hybrid approaches; the belief–desire–intention (BDI) framework is commonly used to structure agent reasoning.

Interactions rely on communication protocols, coordination strategies, and negotiation or market-based mechanisms. Core problems include task allocation, resource sharing, and achieving consensus under partial observability or uncertainty. Methods include distributed constraint optimization, game-theoretic analysis, and multi-agent reinforcement learning, which extends learning to settings with multiple adaptive agents.

Applications include autonomous vehicles, robotic swarms, distributed sensor networks, smart grids, e-commerce, and disaster response. Challenges involve scalability to many agents, non-stationarity as agents learn, reliability and security, privacy, and ensuring safe collaboration. Evaluation emphasizes efficiency, robustness, fairness, and convergence properties.