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Multi-agent system

A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.[1] Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve.[2] Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.[3]

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 do not 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.[4] Applications where multi-agent systems research may deliver an appropriate approach include online trading,[5] disaster response,[6][7] target surveillance[8] and social structure modelling.[9]

Passive agents or "agent without goals" (such as obstacle, apple or key in any simple simulation)

[10]

Active agents with simple goals (like birds in flocking, or wolf–sheep in prey-predator model)

[10]

Cognitive agents (complex calculations)

agent-oriented software engineering

beliefs, desires, and intentions ()

BDI

cooperation and coordination

(DCOPs)

distributed constraint optimization

organization

communication

negotiation

distributed problem solving

[17]

multi-agent learning

agent mining

scientific communities (e.g., on biological flocking, language evolution, and economics)[19]

[18]

dependability and fault-tolerance

robotics, multi-robot systems (MRS), robotic clusters

[20]

multi-agent systems also present possible applications in microrobotics, where the physical interaction between the agents are exploited to perform complex tasks such as manipulation and assembly of passive components.[22]

[21]

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."[16] Research topics include:

Frameworks[edit]

Frameworks have emerged that implement common standards (such as the FIPA and OMG MASIF standards).[23] These frameworks e.g. JADE, save time and aid in the standardization of MAS development.[24]


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.[25]

Applications[edit]

MAS have not only been applied in academic research, but also in industry.[26] MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films.[27] 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[28] include transportation,[29] logistics,[30] graphics, manufacturing, power system,[31] smartgrids,[32] and the GIS.


Also, Multi-agent Systems Artificial Intelligence (MAAI) are used for simulating societies, the purpose thereof being helpful in the fields of climate, energy, epidemiology, conflict management, child abuse, ....[33] 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.[33]


Vehicular traffic with controlled autonomous vehicles can be modelling as a multi-agent system involving crowd dynamics.[34] Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents.[35] Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars.[36][37] It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior.

Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. . p. 366. ISBN 978-0-471-49691-5.

John Wiley & Sons

Shoham, Yoav; Leyton-Brown, Kevin (2008). . Cambridge University Press. p. 496. ISBN 978-0-521-89943-7.

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

Mamadou, Tadiou Koné; Shimazu, A.; Nakajima, T. (August 2000). . Knowledge and Information Systems. 2 (2): 1–26.

"The State of the Art in Agent Communication Languages (ACL)"

Hewitt, Carl; Inman, Jeff (November–December 1991). (PDF). IEEE Transactions on Systems, Man, and Cybernetics. 21 (6): 1409–1419. doi:10.1109/21.135685. S2CID 39080989. Archived from the original (PDF) on August 31, 2017.

"DAI Betwixt and Between: From "Intelligent Agents" to Open Systems Science"

The Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS)

Weiss, Gerhard, ed. (1999). Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence. MIT Press.  978-0-262-23203-6.

ISBN

Ferber, Jacques (1999). Multi-Agent Systems: An Introduction to Artificial Intelligence. Addison-Wesley.  978-0-201-36048-6.

ISBN

Weyns, Danny (2010). Architecture-Based Design of Multi-Agent Systems. Springer.  978-3-642-01063-7.

ISBN

(2006). Cognition and Multi-Agent Interaction. Cambridge University Press. ISBN 978-0-521-83964-8.

Sun, Ron

Keil, David; Goldin, Dina (2006). Weyns, Danny; Parunak, Van; Michel, Fabien (eds.). . LNCS 3830. Vol. 3830. Springer. pp. 68–87. doi:10.1007/11678809_5. ISBN 978-3-540-32614-4. {{cite book}}: |journal= ignored (help)

Indirect Interaction in Environments for Multiagent Systems

, published by Springer Science+Business Media Group

Whitestein Series in Software Agent Technologies and Autonomic Computing

Salamon, Tomas (2011). . Bruckner Publishing. ISBN 978-80-904661-1-1.

Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes

; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2

Russell, Stuart J.

Fasli, Maria (2007). Agent-technology for E-commerce. . p. 480. ISBN 978-0-470-03030-1.

John Wiley & Sons

Cao, Longbing, Gorodetsky, Vladimir, Mitkas, Pericles A. (2009). , IEEE Intelligent Systems, vol. 24, no. 3, 64-72.

Agent Mining: The Synergy of Agents and Data Mining