Multi-agent system

Simple reflex agent
Learning agent

A multi-agent system (M.A.S.) is a computerized system composed of multiple interacting intelligent agents within an environment. Multi-agent systems can be used to solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include some methodic, functional, procedural approach, algorithmic search or reinforcement learning. Although there is considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM). The goal of an ABM is to search for explanatory insight into the collective behavior of agents (which don't necessarily need to be "intelligent") obeying simple rules, typically in natural systems, rather than in solving specific practical or engineering problems. The terminology of ABM tends to be used more often in the sciences, and MAS in engineering and technology.[1] Topics where multi-agent systems research may deliver an appropriate approach include online trading,[2] disaster response,[3][4] and modelling social structures.[5]


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,[6][7] humans or human teams. A multi-agent system may contain combined human-agent teams.

Agents can be divided into different types ranging from simple to complex. Some categories suggested to define these types include:

Agent environments can be divided into:

Agent environments can also be organized according to various properties like: accessibility (depending on if it is possible to gather complete information about the environment), determinism (if an action performed in the environment causes a definite effect), dynamics (how many entities influence the environment in the moment), discreteness (whether the number of possible actions in the environment is finite), episodicity (whether agent actions in certain time periods influence other periods),[9] and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making).[10] Agent actions in the environment are typically mediated via an appropriate middleware. This middleware offers a first-class design abstraction for multi-agent systems, providing means to govern resource access and agent coordination.[11]


The agents in a multi-agent system have several important characteristics:[12]

Self-organization and self-steering

Multi-agent systems can manifest self-organization as well as self-steering and other control paradigms and related complex behaviors even when the individual strategies of all their agents are simple. When agents can share knowledge using any agreed language, within the constraints of the system's communication protocol, the approach may lead to a common improvement. Example languages are Knowledge Query Manipulation Language (KQML) or FIPA's Agent Communication Language (ACL).

Systems paradigms

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

 Speed-VERY_IMPORTANT: min=45 mph, 
 Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40, 
 Contract Priority-REGULAR 

and a weighted response matrix, e.g.

 Speed-min:50 but only if weather sunny,  
 Path length:25 for sunny / 46 for rainy
 Contract Priority-REGULAR
 note - ambulance will override this priority and you'll have to wait

A challenge-response-contract scheme is common in MAS systems, where

 First a "Who can?" question is distributed.
 Only the relevant components respond: "I can, at this price".
 Finally, a contract is set up, usually in several more short communication steps between sides, 

also considering other components, evolving "contracts", and the restriction sets of the component algorithms.

Another paradigm commonly used with MAS systems is the pheromone, where components "leave" information for other components "next in line" or "in the vicinity". These "pheromones" may "evaporate" with time, that is their values may decrease (or increase) with time.


M.A. systems, also referred to as "self-organized systems", tend to find the best solution for their problems "without intervention". There is high similarity here to physical phenomena, such as energy minimizing, where physical objects tend to reach the lowest energy possible within the physically constrained world. For example: many of the cars entering a metropolis in the morning will be available for leaving that same metropolis in the evening.

The main feature which is achieved when developing multi-agent systems, is flexibility, since a multi-agent system can be added to, modified and reconstructed, without the need for detailed rewriting of the application.[14] The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.

Study of multi-agent systems

The study of multi-agent systems is "concerned with the development and analysis of sophisticated AI problem-solving and control architectures for both single-agent and multiple-agent systems."[15] Topics of research in MAS include:


While ad hoc multi-agent systems are often created from scratch by researchers and developers, some frameworks have arisen that implement common standards (such as the FIPA agent system platforms and communication languages). These frameworks save developers time and also aid in the standardization of MAS development. One such developmental framework for robotics is given in .[19] See also Comparison of agent-based modeling software.

Applications in the real world

Multi-agent systems are applied in the real world to graphical applications such as computer games. Agent systems have been used in films.[20] They are also used for coordinated defence systems. Other applications include transportation,[21] logistics,[22] graphics, GIS as well as in many other fields. It is widely being advocated for use in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability, and self-healing networks.

See also


  1. Niazi, Muaz; Hussain, Amir (2011). "Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey" (PDF). Scientometrics. Springer. 89 (2): 479–499. doi:10.1007/s11192-011-0468-9.
  2. Rogers, Alex; David, E.; Schiff, J.; Jennings, N.R. (2007). "The Effects of Proxy Bidding and Minimum Bid Increments within eBay Auctions". ACM Transactions on the Web.
  3. Schurr, Nathan; Marecki, Janusz; Tambe, Milind; Scerri, Paul; Kasinadhuni, Nikhil; Lewis, J.P. (2005). "The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO" (PDF).
  4. Genc, Zulkuf; et al. (2013). "Agent-based information infrastructure for disaster management]" (PDF). Intelligent Systems for Crisis Management: 349–355.
  5. Sun, Ron; Naveh, Isaac. "Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model". Journal of Artificial Societies and Social Simulation.
  6. Kaminka, G. A. (December 2004). "Robots are Agents, Too!]". AgentLink News: 16–17.
  7. Ghasemlou, Shervin, Ali Mohades, Taher Abbas Shangari, and Mohammadreza Tavassoli. "Homecoming: A multi-robot exploration method for conjunct environments with a systematic return procedure." In European Conference on Multi-Agent Systems, pp. 111-127. Springer International Publishing, 2014.
  8. 1 2 Kubera, Yoann; Mathieu, Philippe; Picault, Sébastien (2010), "Everything can be Agent!" (PDF), Proceedings of the ninth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'2010), Toronto, Canada: 1547–1548
  9. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
  10. Salamon, Tomas (2011). Design of Agent-Based Models. Repin: Bruckner Publishing. p. 22. ISBN 978-80-904661-1-1.
  11. Weyns, Danny; Omicini, Amdrea; Odell, James (2007). "Environment as a first-class abstraction in multiagent systems" (PDF). Autonomous Agents and Multi-Agent Systems. 14 (1): 5–30. doi:10.1007/s10458-006-0012-0. Retrieved 2013-05-31.
  12. Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. p. 366. ISBN 0-471-49691-X.
  13. Panait, Liviu; Luke, Sean (2005). "Cooperative Multi-Agent Learning: The State of the Art" (PDF). Autonomous Agents and Multi-Agent Systems. 11 (3): 387–434. doi:10.1007/s10458-005-2631-2.
  14. Rzevski & Skobelev, Managing Complexity (2014) Wit Press
  15. "The Multi-Agent Systems Lab". University of Massachusetts Amherst. Retrieved Oct 16, 2009.
  16. Cucker, Felipe; Steve Smale (2007). "The Mathematics of Emergence" (PDF). Japanese Journal of Mathematics. 2: 197. doi:10.1007/s11537-007-0647-x. Retrieved 2008-06-09.
  17. Shen, Jackie (Jianhong) (2008). "Cucker–Smale Flocking under Hierarchical Leadership". SIAM J. Applied Math. 68 (3): 694. doi:10.1137/060673254. Retrieved 2008-06-09.
  18. Ahmed, S.; Karsiti, M.N. (2007). "A testbed for control schemes using multi agent nonholonomic robots". 2007 IEEE International Conference on Electro/Information Technology. doi:10.1109/EIT.2007.4374547.
  19. Ahmed, Salman; Karsiti, Mohd N.; Agustiawan, Herman (2007). "A development framework for collaborative robots using feedback control".
  20. "Film showcase". MASSIVE. Retrieved 28 April 2012.
  21. Xiao-Feng Xie, S. Smith, G. Barlow. Schedule-driven coordination for real-time traffic network control. International Conference on Automated Planning and Scheduling (ICAPS), Sao Paulo, Brazil, 2012: 323-331.
  22. Máhr, T. S.; Srour, J.; De Weerdt, M.; Zuidwijk, R. (2010). "Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty". Transportation Research Part C: Emerging Technologies. 18: 99. doi:10.1016/j.trc.2009.04.018.

Further reading

External links

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