Single agent vs multi agent system in AI

Single agent vs multi agent system

A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. Despite considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM). Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams.

Single-agent systems should be simpler than multiagent systems when dealing with a fixed, complex task, the opposite is often the case. Single-agent systems belong at the end of the progression from simple to complex multiagent systems. The agent in a single-agent system model itself, the environment, and their interactions. In a single-agent system, no other such entities are recognized by the agent.

Single-agent vs multi-agent system

  • When there is only one agent in a defined environment, it is named the Single-Agent System (SAS). This agent acts and interacts only with its environment.

  • If there is more than one agent and they interact with each other and their environment, the system is called the Multi-Agent System.

  • Single-agent systems are centralized.

  • Centralized systems have a single agent which makes all the decisions, while the others act as remote slaves.

  • A "single-agent system'' should be thought of as a complex, centralized system in a domain which also allows for a multi-agent approach.

  • Multiagent systems differ from single-agent systems most significantly in that the environment's dynamics can be determined by other agents. In addition to the uncertainty that may be inherent in the domain, other agents intentionally affect the environment in unpredictable ways. Thus, all multiagent systems can be viewed as having dynamic environments.

  • A single-agent system might still have multiple entities - several actuators, or even several robots. However, if each entity sends its perceptions to and receives its actions from a single central processor, then there is only a single agent: the central process.

  • Multi-agent systems can manifest self-organization as well as self-direction and other control paradigms and related complex behaviors even when the individual strategies of all their agents are simple.

  • The central agent models all of the entities as a single ``self.'' This section compares the single-agent and multiagent approaches.

  • Many MAS are implemented in computer simulations, stepping the system through discrete "time steps". The MAS components communicate typically using a weighted request matrix.

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