A multi-agent system (MAS) is a framework composed of multiple agents that interact with one another to achieve specific goals or solve problems. An agent in this context can be considered as an autonomous entity that perceives its environment, makes decisions based on those perceptions, and takes actions accordingly. These agents can be software programs, robots, or any other computational entities that can operate independently. The key characteristic of a MAS is the collaboration and coordination among agents, which allows them to handle complex tasks more efficiently than a single agent could.
In a multi-agent system, each agent typically has its own unique capabilities and knowledge. For example, in a logistics application, one agent might specialize in route optimization, while another may focus on inventory management. By communicating and sharing information, these agents can collectively make better decisions. For instance, if the route optimization agent finds a traffic jam, it can inform the inventory management agent to adjust delivery schedules accordingly. The interaction between agents can happen through direct communication, or through shared environments where they send signals and responses based on certain events.
Multi-agent systems can be applied in various domains, ranging from artificial intelligence research to real-world applications. In autonomous vehicle coordination, for example, multiple vehicles (agents) communicate to avoid collisions and optimize traffic flow. Similarly, in online gaming, different non-player characters (NPCs) can represent agents that interact in a shared game world, enabling more realistic and engaging gameplay. Overall, multi-agent systems provide a structured way to develop systems where distributed, autonomous entities work together to achieve results that are often beyond the reach of individual agents.