Multi-agent system

Last updated
Simple reflex agent IntelligentAgent-SimpleReflex.png
Simple reflex agent
Learning agent IntelligentAgent-Learning.png
Learning agent

A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents [ citation needed ]. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. [1] Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. [2]


Despite 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 science, and MAS in engineering and technology. [3] Applications where multi-agent systems research may deliver an appropriate approach include online trading, [4] disaster response [5] [6] and social structure modelling. [7]


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:

Agent environments can be divided into:

Agent environments can also be organized according to properties such as accessibility (whether it is possible to gather complete information about the environment), determinism (whether an action 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), [9] and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making). [10] 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. [11]


The agents in a multi-agent system have several important characteristics: [12]

Self-organisation and self-direction

Multi-agent systems can manifest self-organisation as well as self-direction and other control paradigms and related complex behaviors even when the individual strategies of all their agents are simple.[ citation needed ] 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 Agent Communication Language (ACL).

System paradigms

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

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 (or increase).


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.


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." [14] Research topics include:


Frameworks have emerged that implement common standards (such as the FIPA and OMG MASIF [19] standards). These frameworks e.g. JADE, save time and aid in the standardization of MAS development. [20]

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. [21]


MAS have not only been applied in academic research, but also in industry. [22] MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films. [23] They are used for coordinated defence systems. Other applications [24] include transportation, [25] logistics, [26] graphics, manufacturing, power system [27] , smartgrids [28] and GIS. It is widely advocated for use in networking and mobile technologies, to achieve automatic and dynamic load balancing, high scalability and self-healing networks.

See also

Related Research Articles

Distributed Artificial Intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems.

In computer science, a software agent is a computer program that acts for a user or other program in a relationship of agency, which derives from the Latin agere : an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Agents are colloquially known as bots, from robot. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a phone or other computing device. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form.

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

Crowd simulation

Crowd simulation is the process of simulating the movement of a large number of entities or characters. It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, architecture and urban planning, and evacuation simulation.

An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts.

A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. One of the main goals of a cognitive architecture is to summarize the various results of cognitive psychology in a comprehensive computer model. However, the results need to be formalized so far as they can be the basis of a computer program. The formalized models can be used to further refine a comprehensive theory of cognition, and more immediately, as a commercially usable model. Successful cognitive architectures include ACT-R and SOAR.

In computer science multi-agent planning involves coordinating the resources and activities of multiple agents.

In artificial intelligence, a procedural reasoning system (PRS) is a framework for constructing real-time reasoning systems that can perform complex tasks in dynamic environments. It is based on the notion of a rational agent or intelligent agent using the belief–desire–intention software model.

The agent systems reference model (ASRM) is a layered, abstract description for multiagent systems. As such, the reference model

A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system.

Robotics middleware is middleware to be used in complex robot control software systems.

Agent-based social simulation consists of social simulations that are based on agent-based modeling, and implemented using artificial agent technologies. Agent-based social simulation is scientific discipline concerned with simulation of social phenomena, using computer-based multiagent models. In these simulations, persons or group of persons are represented by agents. MABSS is combination of social science, multiagent simulation and computer simulation.

Agent-oriented programming (AOP) is a programming paradigm where the construction of the software is centered on the concept of software agents. In contrast to object-oriented programming which has objects at its core, AOP has externally specified agents at its core. They can be thought of as abstractions of objects. Exchanged messages are interpreted by receiving "agents", in a way specific to its class of agents.

Juan Pavón Spanish computer scientist (b.1962)

Juan Pavón is a Spanish computer scientist, full professor of the Complutense University of Madrid (UCM). He is a pioneer researcher in the field of Software Agents, co-creator of the FIPA MESSAGE and INGENIAS methodologies, and founder and director of the research group GRASIA: GRoup of Agent-based, Social and Interdisciplinary Applications at UCM. He is known for his work in the field of Artificial Intelligence, specifically in agent-oriented software engineering.


INGENIAS is an open-source software framework for the analysis, design and implementation of multi-agent systems (MAS).

Knowledge Engineering and Machine Learning Group

The Knowledge Engineering and Machine Learning group (KEMLg) is a research group belonging to the Technical University of Catalonia (UPC) – BarcelonaTech. It was founded by Prof. Ulises Cortés. The group has been active in the Artificial Intelligence field since 1986.

Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centred on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent. Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low cost, smarter robots have intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.

Agent mining is an interdisciplinary area that synergizes multiagent systems with data mining and machine learning.

This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.


  1. "A Multi Agent-Based System for Securing University Campus: Design and Architecture - IEEE Conference Publication". 2019-12-17. Retrieved 2020-01-23.
  2. "Multi Agent Systems - an overview". ScienceDirect Topics. 2016-01-01. Retrieved 2020-01-23.
  3. Niazi, Muaz; Hussain, Amir (2011). "Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey" (PDF). Scientometrics. 89 (2): 479–499. arXiv: 1708.05872 . doi:10.1007/s11192-011-0468-9.
  4. Rogers, Alex; David, E.; Schiff, J.; Jennings, N.R. (2007). "The Effects of Proxy Bidding and Minimum Bid Increments within eBay Auctions". ACM Transactions on the Web. 1 (2): 9–es. CiteSeerX . doi:10.1145/1255438.1255441.
  5. Schurr, Nathan; Marecki, Janusz; Tambe, Milind; Scerri, Paul; Kasinadhuni, Nikhil; Lewis, J.P. (2005). "The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO" (PDF).Cite journal requires |journal= (help)
  6. Genc, Zulkuf; et al. (2013). "Agent-based information infrastructure for disaster management" (PDF). Intelligent Systems for Crisis Management: 349–355.
  7. Sun, Ron; Naveh, Isaac. "Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model". Journal of Artificial Societies and Social Simulation.
  8. 1 2 Kubera, Yoann; Mathieu, Philippe; Picault, Sébastien (2010), "Everything can be Agent!" (PDF), Proceedings of the Ninth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'2010): 1547–1548
  9. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN   0-13-790395-2
  10. Salamon, Tomas (2011). Design of Agent-Based Models. Repin: Bruckner Publishing. p. 22. ISBN   978-80-904661-1-1.
  11. Weyns, Danny; Omicini, Amdrea; Odell, James (2007). "Environment as a first-class abstraction in multiagent systems" (PDF). Autonomous Agents and Multi-Agent Systems. 14 (1): 5–30. CiteSeerX . doi:10.1007/s10458-006-0012-0 . Retrieved 2013-05-31.[ permanent dead link ]
  12. Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. p. 366. ISBN   978-0-471-49691-5.
  13. Panait, Liviu; Luke, Sean (2005). "Cooperative Multi-Agent Learning: The State of the Art" (PDF). Autonomous Agents and Multi-Agent Systems. 11 (3): 387–434. CiteSeerX . doi:10.1007/s10458-005-2631-2.
  14. "The Multi-Agent Systems Lab". University of Massachusetts Amherst . Retrieved Oct 16, 2009.
  15. Albrecht, Stefano; Stone, Peter (2017), "Multiagent Learning: Foundations and Recent Trends. Tutorial", IJCAI-17 conference (PDF)
  16. Cucker, Felipe; Steve Smale (2007). "The Mathematics of Emergence" (PDF). Japanese Journal of Mathematics. 2: 197–227. doi:10.1007/s11537-007-0647-x . Retrieved 2008-06-09.
  17. Shen, Jackie (Jianhong) (2008). "Cucker–Smale Flocking under Hierarchical Leadership". SIAM J. Appl. Math. 68 (3): 694–719. arXiv: q-bio/0610048 . doi:10.1137/060673254 . Retrieved 2008-06-09.
  18. Ahmed, S.; Karsiti, M.N. (2007), "A testbed for control schemes using multi agent nonholonomic robots", 2007 IEEE International Conference on Electro/Information Technology, p. 459, doi:10.1109/EIT.2007.4374547, ISBN   978-1-4244-0940-2
  19. "OMG Document – orbos/97-10-05 (Update of Revised MAF Submission)". Retrieved 2019-02-19.
  20. Ahmed, Salman; Karsiti, Mohd N.; Agustiawan, Herman (2007). "A development framework for collaborative robots using feedback control". CiteSeerX .Cite journal requires |journal= (help)
  21. "IEEE IES Technical Committee on Industrial Agents (TC-IA)". Retrieved 2019-02-19.
  22. Leitão, Paulo; Karnouskos, Stamatis (2015-03-26). Industrial agents : emerging applications of software agents in industry. Leitão, Paulo,, Karnouskos, Stamatis. Amsterdam, Netherlands. ISBN   978-0128003411. OCLC   905853947.
  23. "Film showcase". MASSIVE . Retrieved 28 April 2012.
  24. Leitao, Paulo; Karnouskos, Stamatis; Ribeiro, Luis; Lee, Jay; Strasser, Thomas; Colombo, Armando W. (2016). "Smart Agents in Industrial Cyber–Physical Systems". Proceedings of the IEEE. 104 (5): 1086–1101. doi:10.1109/JPROC.2016.2521931. ISSN   0018-9219.
  25. Xiao-Feng Xie, S. Smith, G. Barlow. Schedule-driven coordination for real-time traffic network control. International Conference on Automated Planning and Scheduling (ICAPS), São Paulo, Brazil, 2012: 323–331.
  26. Máhr, T. S.; Srour, J.; De Weerdt, M.; Zuidwijk, R. (2010). "Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty". Transportation Research Part C: Emerging Technologies. 18: 99–119. CiteSeerX . doi:10.1016/j.trc.2009.04.018.
  27. "Generation Expansion Planning Considering Investment Dynamic of Market Participants Using Multi-agent System - IEEE Conference Publication". 2019-12-17. Retrieved 2020-01-23.
  28. "Distributed Multi-Agent System-Based Load Frequency Control for Multi-Area Power System in Smart Grid - IEEE Journals & Magazine". 2019-12-17. Retrieved 2020-01-23.

Further reading