Multi-Agent Systems

What are Multi-Agent Systems?

In Artificial Intelligence research agent systems have been recognized as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are computer programs that act autonomously on behalf of their users, across open and distributed environments, to solve problems. However, applications require multiple agents that can work together. A Multi-Agent System (MAS) is a loosely coupled network of software agents that can work together and solve problems that are difficult or impossible for an individual agent or a monolithic system to slove.


Simple Reflex Agents


Model Based Reflex Agents

goal_based_agents (1)

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Agents can be divided into:

  • Active Agents – Agents having certain goals
  • Passive Agents – Agents without goals
  • Cognitive Agents – Agents with complex goals or operations.

Now depending on the environment the agents are working in can be classified as:

  • Discrete Environment – A discrete environment has fixed locations or time intervals.
  • Continuous Environment – A continuous environment could be measured quantitatively to any level of precision.


Advantages of Multi-Agent Systems:

  • A MAS distributes computational resources and capabilities across a network of interconnected agents. Whereas a centralized system maybe plagued by resource limitations, performance bottlenecks or resource limitations.
  • A MAS allows for the interconnection and interpolation of multiple existing systems.
  • A MAS models problems in terms of autonomous intteracting component-agents which is proving to be a more natural way of representing task allocation, team planning, user preferences, open environments and so on.
  •  A MAS provides solutions in situtations where expertise is temporally and spatially distributed.



  • Intelligent monitoring of Airline Operations.
  • Self-healing networks.
  • Milatry Logistics Planning.



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