A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. Despite considerable overlap, a multi-agent system is not always the same as an agent-based model. The goal of an ABM is to search for explanatory insight into the collective behavior of agents 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 science, and MAS in engineering and technology. Applications where multi-agent systems research may deliver an appropriate approach include online trading, disaster response and social structure modelling.
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 types spanning simple to complex. Categories include:
Passive agents or "agent without goals"
Active agents with simple goals
Cognitive agents
Agent environments can be divided into:
Virtual
Discrete
Continuous
Agent environments can also be organized according to properties such as accessibility, determinism, dynamics, discreteness, episodicity, and dimensionality. Agent actions 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: agents at least partially independent, self-aware, autonomous
Local views: no agent has a full global view, or the system is too complex for an agent to exploit such knowledge
Decentralization: no agent is designated as controlling
Many MAS 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 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 is the "pheromone", where components leave information for other nearby components. These pheromones may evaporate/concentrate with time, that is their values may decrease.
Properties
MAS 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 systems also tend to prevent propagation of faults, self-recover and be fault tolerant, mainly due to the redundancy of components.
Research
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." Research topics include:
Frameworks have emerged that implement common standards. These frameworks e.g. JADE, save time and aid in the standardization of MAS development. Currently though, no standard is actively maintained from FIPA or OMG. Efforts for further development of software agents in industrial context are carried out in IEEE IES technical committee on Industrial Agents.
Applications
MAS have not only been applied in academic research, but also in industry. MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films. It is widely advocated for use in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability and self-healing networks. They are being used for coordinated defence systems. Other applications include transportation, logistics, graphics, manufacturing, power system, smartgrids and GIS. Also, Multi-agent Systems Artificial Intelligence are used for simulating societies, the purpose thereof being helpful in the fields of climate, energy, epidemiology, conflict management, child abuse,.... Some organisations working on using multi-agent system models include Center for Modelling Social Systems, Centre for Research in Social Simulation, Centre for Policy Modelling, Society for Modelling and Simulation International.
