Multi-agent system

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Learning agent

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Examples of problems which are appropriate to multi-agent systems research include online trading,[1] disaster response,[2] and modelling social structures.[3]


[edit] 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]

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).

[edit] Multi-agent system basics

[edit] Multiple agent systems paradigms

Many MAS 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=45mph, 
 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.

[edit] 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.

[edit] 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:

  • agent-oriented software engineering
  • beliefs, desires, and intentions (BDI)
  • cooperation and coordination
  • organisation
  • communication
  • negotiation
  • distributed problem solving
  • multi-agent learning
  • scientific communities
  • dependability and fault-tolerance

[edit] 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. Examples of such frameworks are:

[edit] 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 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.

[edit] See also

[edit] References

  1. ^ Alex Rogers and E. David and J.Schiff and N.R. Jennings. The Effects of Proxy Bidding and Minimum Bid Increments within eBay Auctions, ACM Transactions on the Web, 2007
  2. ^ Nathan Schurr and Janusz Marecki and Milind Tambe and Paul Scerri The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO, 2005.
  3. ^ Ron Sun and Isaac Naveh. Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model, Journal of Artificial Societies and Social Simulation.
  4. ^ Michael Wooldridge, An Introduction to MultiAgent Systems, John Wiley & Sons Ltd, 2002, paperback, 366 pages, ISBN 0-471-49691-X.
  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)
  6. ^ Massive (software)Film showcase

[edit] Further reading

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