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Multi-agent system

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A multi-agent system (MAS) is a system composed of several software agents, collectively capable of reaching goals that are difficult to achieve by an individual agent or monolithic system.

Overview

The exact nature of the agents is a matter of some controversy. They are sometimes claimed to be autonomous. For example a household floor cleaning robot can be autonomous in that it is dependent on a human operator only to start it up. On the other hand, in practice, all agents are under active human supervision. Furthermore, the more important the activities of the agent are to humans, the more supervision that they receive. In fact, autonomy is seldom desired. Instead interdependent systems are needed.

MAS can be claimed to include human agents as well. Human organizations and society in general can be considered an example of a multi-agent system. The Wikipedia community could also be considered a multi-agent system, as explained below.

Multi-agent systems can manifest self-organization and complex behaviors even when the individual strategies of all their agents are simple.

To share knowledge agents can use Knowledge Query Manipulation Language (KQML) or FIPA's Agent Communication Language (ACL).

Multi-agent system: Topics

The study of multi-agent systems

The study of Multi-Agent Systems is concerned with the development and analysis of sophisticated Artificial intelligence problem solving and control architectures for both single-agent and multiple-agent systems.[citation needed]

Topics of research in MAS include:

  1. beliefs, desires, and intentions (BDI),
  2. cooperation and coordination,
  3. organisation,
  4. communication,
  5. negotiation,
  6. distributed problem solving,
  7. multi-agent learning.
  8. scientific communities
  9. dependability and fault-tolerance

Multiple agent systems paradigms

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

 Speed-VERY_IMPORTANT: min=45mph, 
 Path length-MEDIUM_IMPORTANCE: max=60 expectedMax=40, 
 Max-Weight-UNIMPORTANT 
 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.

Properties

MAS systems are also referred to as "self-organized systems" as they 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 physical 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 MAS systems, if they work, is flexibility, since a MAS system can be added to, modified and reconstructed, without the need for detailed rewriting of the application. These systems also tend to be rapidly self-recovering and failure proof, usually due to the heavy redundancy of components and the self managed features, referred to, above.

Applications in the real world

Although MAS is still strictly a research topic, many graphic computer games today are developed using MAS algorithms and MAS frameworks. MAS is applicable in transportation, logistics, graphics, GIS systems as well as in many other fields. It is widely being advocated to be used in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability, and self healing networks.

See also

References

Further reading

  • Michael Wooldridge, An Introduction to MultiAgent Systems, John Wiley & Sons Ltd, 2002, paperback, 366 pages, ISBN 0-471-49691-X.
  • Carl Hewitt and Jeff Inman. DAI Betwixt and Between: From "Intelligent Agents" to Open Systems Science IEEE Transactions on Systems, Man, and Cybernetics. Nov./Dec. 1991.
  • The Journal of Autonomous Agents and Multiagent Systems, Publisher: Springer Science+Business Media B.V., formerly Kluwer Academic Publishers B.V. [1]
  • Gerhard Weiss, ed. by, Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999, ISBN 0-262-23203-0.
  • Jacques Ferber, Multi-Agent Systems: An Introduction to Artificial Intelligence, Addison-Wesley, 1999, ISBN 0-201-36048-9.
  • Sun, Ron, (2006). "Cognition and Multi-Agent Interaction". Cambridge University Press. http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=0521839645
  • José M. Vidal, Fundamentals of Multiagent Systems: with NetLogo Examples.
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