A multiple agent system (MAS) is a distributed parallel computer system built of many very simple components, each using a simple algorithm, and each communicating with other components. A paradigm of an ant colony or bee swarm is used many times. The ants have a very simple algorithm, yet an "emerging intelligence" is received from the mass of ants, communicating with each other, and always, finally, "finding the sugar".

Multiple agent systems paradigms

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

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

and a weighted response matrix (i.e.

 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 increace) 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 reconsturcted, 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 redundency 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 ballancing, high scalability, and self healing networks.

See also

• Ant colony optimization (ACO), Emergence, Swarm intelligence, artificial intelligence (AI), artificial life, evolutionary algorithms, graph theory, virtual pheromones .

External links

• MAS information
• Swarm software framework open source framework available for developing and researching MAS.
• Description of emergence on a large scale