Multi-agent system

File:IntelligentAgent-SimpleReflex.png

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. Topics where multi-agent systems research may deliver an appropriate approach include online trading, disaster response, and modelling social structures.

Concept

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, 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:

• Passive agents or agent without goals (like obstacle, apple or key in any simple simulation)
• Active agents with simple goals (like birds in flocking, or wolf–sheep in prey-predator model)
• Cognitive agents (which contain complex calculations)

Agent environments can be divided into:

• Virtual Environment
• Discrete Environment
• Continuous Environment

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), and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making). 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.

Characteristics

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

• Autonomy: the agents are at least partially independent, self-aware, 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 designated controlling agent (or the system is effectively reduced to a monolithic system)

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, 
 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

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. The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.

The 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." Topics of research in MAS include:

• agent-oriented software engineering
• beliefs, desires, and intentions (BDI)
• cooperation and coordination
• Distributed constraint optimization (DCOPs)
• organization
• communication
• negotiation
• distributed problem solving
• multi-agent learning
• scientific communities (e.g., on biological flocking, language evolution, and economics)
• dependability and fault-tolerance
• robotics, Multi-robot systems (MRS), Robotic clusters

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 developmental framework for robotics is given in . 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. 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.

See also

• Agent-based model (ABM)
• Comparison of agent-based modeling software
• Agent-based computational economics (ACE)
• Artificial brain
• Artificial intelligence
• Artificial life
• Artificial life framework
• Complex systems
• Discrete event simulation
• Distributed artificial intelligence
• Emergence
• Evolutionary computation
• Human-based genetic algorithm
• Intelligent agent
• Knowledge Query and Manipulation Language (KQML)
• Microbial intelligence
• Multi-agent planning
• Pattern-oriented modeling
• PlatBox Project
• Reinforcement learning
• Robotic clusters
• Scientific Community Metaphor
• Self-organization
• Self-Reconfiguring Modular Robotics
• Simulated reality
• Social simulation
• Software agent
• Swarm Intelligence

Further reading

• The Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS)
• Whitestein Series in Software Agent Technologies and Autonomic Computing, published by Springer Science+Business Media Group

External links

• Random Agent-Based Simulations by Borys Biletskyy – Random agent-base simulations for multi-robot system and Belousov-Zhabotinsky reaction. Java applets available.
• CORMAS (COmmon Resources Multi-Agent System) An open-source framework for Multi-Agent Systems based on SmallTalk. Spatialized, it focuses on issues related to natural resource management and negotiation between stakeholders.
• JaCaMo MAS Platform - An open-source platform for Multi-Agent Systems based on Jason, CArtAgO, and Moise.
• Janus multiagent Platform – Holonic multiagent execution platform (Apache License), written in Java, and directly supporting the SARL agent-oriented programming language.
• HarTech Technologies - HarTech Technologies developed a dedicated Distributed Multi Agent System Framework used in both simulation and large scale command and control system. This unique framework called the Generic Blackboard (GBB) provides a development framework for such systems which is domain independent. Distributed Multi Agent Framework.
• MaDKit is a lightweight open source Java library for designing and simulating Multi-Agent Systems. MaDKit is built upon the AGR (Agent/Group/Role) organizational model: agents are situated in groups and play roles, MAS are conceived as articficial societies.
• Agent Factory Agent Factory is an open source collection of tools, platforms, and languages that support the development and deployment of Multi-Agent Systems.