Simple reflex agent

Learning agent

Overview

The agents in a multi-agent system have several important characteristics^[4]:

• Autonomy: the agents are at least partially autonomous
• Local views: no agent has a full global view of the system, or the system is too complex for an agent to make practical use of such knowledge
• Decentralization: there is no one controlling agent (or the system is effectively reduced to a monolithic system) ^[5]

Liviu Panait, Sean Luke: Cooperative Multi-Agent Learning: The State of the Art. Autonomous Agents and Multi-Agent Systems 11(3): 387-434 (2005)

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.

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

Agents can share knowledge using any agreed language, within the constraints of the system's communication protocol. Example languages are Knowledge Query Manipulation Language (KQML) or FIPA's Agent Communication Language (ACL).

Multi-agent system basics

Multiple agent systems paradigms

Many MAS systems are implemented in computer emulations, stepping the system through discrete "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, 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 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.

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 multiple-agent systems.

Topics of research in MAS include:

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

Frameworks

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 framework is Jade.

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.^[6]. They are also used for coordinated defence systems. Other applications include 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

• Agent-based model
• Artificial brain
• Artificial intelligence
• Complex systems
• Cougaar
• Distributed artificial intelligence
• Emergence
• Evolutionary computation
• FIPA
• GNUBrain: Implementation of a multi agent framework (GPL)
• Human-based genetic algorithm

• Intelligent agent
• KQML
• Multi-agent planning
• Pattern oriented modeling
• PlatBox Project
• Scientific Community Metaphor
• Self-organization
• Simulated reality
• Social simulation
• Software agent
• Swarm Intelligence

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. http://www.springerlink.com/app/home/journal.asp?wasp=1f1b3716d3d0466b8ab8af5930d8cf68&referrer=parent&backto=linkingpublicationresults,1:102852,1
• 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.
• David Keil, Dina Goldin. Indirect Interaction in Environments for Multiagent Systems (PDF). In Environments for Multiagent Systems II, eds. Danny Weyns, Van Parunak, Fabien Michel. LNCS 3830, Springer, 2006.
• Whitestein Series in Software Agent Technologies and Autonomic Computing, published by Springer Science+Business Media Group

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