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Multi-agent system

November 30, 2023

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][4]

Simple reflex agent
Learning agent

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.[5] Applications where multi-agent systems research may deliver an appropriate approach include online trading,[6] disaster response,[7][8] target surveillance [9] and social structure modelling.[10]

Concept edit

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[11] or "agent without goals" (such as obstacle, apple or key in any simple simulation)
  • Active agents[11] with simple goals (like birds in flocking, or wolf–sheep in prey-predator model)
  • Cognitive agents (complex calculations)

Agent environments can be divided into:

  • Virtual
  • Discrete
  • Continuous

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),[12] and dimensionality (whether spatial characteristics are important factors of the environment and the agent considers space in its decision making).[13] 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.[14]

Characteristics edit

The agents in a multi-agent system have several important characteristics:[15]

  • 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 (or the system is effectively reduced to a monolithic system)[16]

Self-organisation and self-direction edit

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 edit

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 (or increase).

Properties edit

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 edit

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."[17] Research topics include:

Frameworks edit

Frameworks have emerged that implement common standards (such as the FIPA and OMG MASIF[24] standards). These frameworks e.g. JADE, save time and aid in the standardization of MAS development.[25]

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.[26]

Applications edit

MAS have not only been applied in academic research, but also in industry.[27] MAS are applied in the real world to graphical applications such as computer games. Agent systems have been used in films.[28] 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[29] include transportation,[30] logistics,[31] graphics, manufacturing, power system,[32] smartgrids[33] and 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, ....[34] 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.[34]

Vehicular traffic with controlled autonomous vehicles can be modelling as a multi-agent system involving crowd dynamics.[35] 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.[36] Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars.[37][38] 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.

See also edit

References edit

  1. ^ Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A., "A Decentralized Cluster Formation Containment Framework for Multirobot Systems" IEEE Transactions on Robotics, 2021.
  2. ^ Hu, J.; Turgut, A.; Lennox, B.; Arvin, F., "Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances: Design and Experiments" IEEE Transactions on Circuits and Systems II: Express Briefs, 2021.
  3. ^ Hu, J.; Bhowmick, P.; Lanzon, A., "Group Coordinated Control of Networked Mobile Robots with Applications to Object Transportation" IEEE Transactions on Vehicular Technology, 2021.
  4. ^ Wiering, M. A. (2000). "Multi-agent reinforcement learning for traffic light control". Machine Learning: Proceedings of the Seventeenth International Conference (Icml'2000): 1151–1158. hdl:1874/20827.
  5. ^ 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. S2CID 17934527.
  6. ^ Rogers, Alex; David, E.; Schiff, J.; Jennings, N.R. (2007). . ACM Transactions on the Web. 1 (2): 9–es. CiteSeerX 10.1.1.65.4539. doi:10.1145/1255438.1255441. S2CID 207163424. Archived from the original on April 2, 2010. Retrieved March 18, 2008.
  7. ^ Schurr, Nathan; Marecki, Janusz; Tambe, Milind; Scerri, Paul; Kasinadhuni, Nikhil; Lewis, J.P. (2005). (PDF). Archived from the original (PDF) on June 3, 2013. Retrieved April 28, 2012. {{cite journal}}: Cite journal requires |journal= (help)
  8. ^ Genc, Zulkuf; et al. (2013). "Agent-Based Information Infrastructure for Disaster Management" (PDF). Intelligent Systems for Crisis Management. Lecture Notes in Geoinformation and Cartography. pp. 349–355. doi:10.1007/978-3-642-33218-0_26. ISBN 978-3-642-33217-3.
  9. ^ Hu, Junyan; Bhowmick, Parijat; Lanzon, Alexander (2020). "Distributed Adaptive Time-Varying Group Formation Tracking for Multiagent Systems With Multiple Leaders on Directed Graphs". IEEE Transactions on Control of Network Systems. 7: 140–150. doi:10.1109/TCNS.2019.2913619. S2CID 149609966.
  10. ^ Sun, Ron; Naveh, Isaac (June 30, 2004). "Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model". Journal of Artificial Societies and Social Simulation.
  11. ^ a b 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
  12. ^ 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
  13. ^ Salamon, Tomas (2011). Design of Agent-Based Models. Repin: Bruckner Publishing. p. 22. ISBN 978-80-904661-1-1.
  14. ^ Weyns, Danny; Omicini, Amdrea; Odell, James (2007). "Environment as a first-class abstraction in multiagent systems". Autonomous Agents and Multi-Agent Systems. 14 (1): 5–30. CiteSeerX 10.1.1.154.4480. doi:10.1007/s10458-006-0012-0. S2CID 13347050.
  15. ^ Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. p. 366. ISBN 978-0-471-49691-5.
  16. ^ 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 10.1.1.307.6671. doi:10.1007/s10458-005-2631-2. S2CID 19706.
  17. ^ "The Multi-Agent Systems Lab". University of Massachusetts Amherst. Retrieved October 16, 2009.
  18. ^ Albrecht, Stefano; Stone, Peter (2017), "Multiagent Learning: Foundations and Recent Trends. Tutorial", IJCAI-17 conference (PDF)
  19. ^ Cucker, Felipe; Steve Smale (2007). "The Mathematics of Emergence" (PDF). Japanese Journal of Mathematics. 2: 197–227. doi:10.1007/s11537-007-0647-x. S2CID 2637067. Retrieved June 9, 2008.
  20. ^ 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. S2CID 14655317. Retrieved June 9, 2008.
  21. ^ 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, S2CID 2734931
  22. ^ Yang, Lidong; Li, Zhang (2021). "Motion control in magnetic microrobotics: From individual and multiple robots to swarms". Annual Review of Control, Robotics, and Autonomous Systems. 4: 509–534. doi:10.1146/annurev-control-032720-104318. S2CID 228892228.
  23. ^ Pinan Basualdo, Franco; Misra, Sarthak (2023). "Collaborative Magnetic Agents for 3D Microrobotic Grasping". Advanced Intelligent Systems. doi:10.1002/aisy.202300365. S2CID 262167298.
  24. ^ "OMG Document – orbos/97-10-05 (Update of Revised MAF Submission)". www.omg.org. Retrieved February 19, 2019.
  25. ^ Ahmed, Salman; Karsiti, Mohd N.; Agustiawan, Herman (2007). "A development framework for collaborative robots using feedback control". CiteSeerX 10.1.1.98.879. {{cite journal}}: Cite journal requires |journal= (help)
  26. ^ "IEEE IES Technical Committee on Industrial Agents (TC-IA)". tcia.ieee-ies.org. Retrieved February 19, 2019.
  27. ^ Leitão, Paulo; Karnouskos, Stamatis (March 26, 2015). Industrial agents : emerging applications of software agents in industry. Leitão, Paulo,, Karnouskos, Stamatis. Amsterdam, Netherlands. ISBN 978-0128003411. OCLC 905853947.{{cite book}}: CS1 maint: location missing publisher (link)
  28. ^ "Film showcase". MASSIVE. Retrieved April 28, 2012.
  29. ^ 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. S2CID 579475.
  30. ^ 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.
  31. ^ 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 10.1.1.153.770. doi:10.1016/j.trc.2009.04.018.
  32. ^ "Generation Expansion Planning Considering Investment Dynamic of Market Participants Using Multi-agent System - IEEE Conference Publication". December 17, 2019. doi:10.1109/SGC.2018.8777904. S2CID 199058301. {{cite journal}}: Cite journal requires |journal= (help)
  33. ^ "Distributed Multi-Agent System-Based Load Frequency Control for Multi-Area Power System in Smart Grid - IEEE Journals & Magazine". December 17, 2019. doi:10.1109/TIE.2017.2668983. S2CID 31816181. {{cite journal}}: Cite journal requires |journal= (help)
  34. ^ a b "AI can predict your future behaviour with powerful new simulations". New Scientist.
  35. ^ Gong, Xiaoqian; Herty, Michael; Piccoli, Benedetto; Visconti, Giuseppe (May 3, 2023). "Crowd Dynamics: Modeling and Control of Multiagent Systems". Annual Review of Control, Robotics, and Autonomous Systems. 6 (1): 261–282. doi:10.1146/annurev-control-060822-123629. ISSN 2573-5144.
  36. ^ Hallerbach, S.; Xia, Y.; Eberle, U.; Koester, F. (2018). "Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles". SAE International Journal of Connected and Automated Vehicles. SAE International. 1 (2): 93. doi:10.4271/2018-01-1066.
  37. ^ Madrigal, Story by Alexis C. "Inside Waymo's Secret World for Training Self-Driving Cars". The Atlantic. Retrieved August 14, 2020.
  38. ^ Connors, J.; Graham, S.; Mailloux, L. (2018). "Cyber Synthetic Modeling for Vehicle-to-Vehicle Applications". In International Conference on Cyber Warfare and Security. Academic Conferences International Limited: 594-XI.

Further reading edit

  • Wooldridge, Michael (2002). An Introduction to MultiAgent Systems. John Wiley & Sons. p. 366. ISBN 978-0-471-49691-5.
  • Shoham, Yoav; Leyton-Brown, Kevin (2008). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press. p. 496. ISBN 978-0-521-89943-7.
  • Mamadou, Tadiou Koné; Shimazu, A.; Nakajima, T. (August 2000). "The State of the Art in Agent Communication Languages (ACL)". Knowledge and Information Systems. 2 (2): 1–26.
  • 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.
  • 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. ISBN 978-0-262-23203-6.
  • Ferber, Jacques (1999). Multi-Agent Systems: An Introduction to Artificial Intelligence. Addison-Wesley. ISBN 978-0-201-36048-6.
  • Weyns, Danny (2010). Architecture-Based Design of Multi-Agent Systems. Springer. ISBN 978-3-642-01063-7.
  • Sun, Ron (2006). Cognition and Multi-Agent Interaction. Cambridge University Press. ISBN 978-0-521-83964-8.
  • Keil, David; Goldin, Dina (2006). Weyns, Danny; Parunak, Van; Michel, Fabien (eds.). Indirect Interaction in Environments for Multiagent Systems. LNCS 3830. Vol. 3830. Springer. pp. 68–87. doi:10.1007/11678809_5. ISBN 978-3-540-32614-4. {{cite book}}: |journal= ignored (help)
  • , published by Springer Science+Business Media Group
  • Salamon, Tomas (2011). Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes. Bruckner Publishing. ISBN 978-80-904661-1-1.
  • 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
  • Fasli, Maria (2007). Agent-technology for E-commerce. John Wiley & Sons. p. 480. ISBN 978-0-470-03030-1.
  • Cao, Longbing, Gorodetsky, Vladimir, Mitkas, Pericles A. (2009). Agent Mining: The Synergy of Agents and Data Mining, IEEE Intelligent Systems, vol. 24, no. 3, 64-72.

November 30, 2023
multi, agent, system, multi, agent, system, self, organized, system, computerized, system, composed, multiple, interacting, intelligent, agents, solve, problems, that, difficult, impossible, individual, agent, monolithic, system, solve, intelligence, include, . 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 4 Simple reflex agentLearning agentDespite 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 5 Applications where multi agent systems research may deliver an appropriate approach include online trading 6 disaster response 7 8 target surveillance 9 and social structure modelling 10 Contents 1 Concept 1 1 Characteristics 1 2 Self organisation and self direction 1 3 System paradigms 1 4 Properties 2 Research 3 Frameworks 4 Applications 5 See also 6 References 7 Further readingConcept editMulti 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 11 or agent without goals such as obstacle apple or key in any simple simulation Active agents 11 with simple goals like birds in flocking or wolf sheep in prey predator model Cognitive agents complex calculations Agent environments can be divided into Virtual Discrete ContinuousAgent 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 12 and dimensionality whether spatial characteristics are important factors of the environment and the agent considers space in its decision making 13 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 14 Characteristics edit The agents in a multi agent system have several important characteristics 15 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 or the system is effectively reduced to a monolithic system 16 Self organisation and self direction edit 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 edit 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 or increase Properties edit 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 editThe 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 17 Research topics 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 18 agent mining scientific communities e g on biological flocking language evolution and economics 19 20 dependability and fault tolerance robotics 21 multi robot systems MRS robotic clusters multi agent systems also present possible applications in microrobotics 22 where the physical interaction between the agents are exploited to perform complex tasks such as manipulation and assembly of passive components 23 Frameworks editFrameworks have emerged that implement common standards such as the FIPA and OMG MASIF 24 standards These frameworks e g JADE save time and aid in the standardization of MAS development 25 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 26 Applications editMAS have not only been applied in academic research but also in industry 27 MAS are applied in the real world to graphical applications such as computer games Agent systems have been used in films 28 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 29 include transportation 30 logistics 31 graphics manufacturing power system 32 smartgrids 33 and 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 34 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 34 Vehicular traffic with controlled autonomous vehicles can be modelling as a multi agent system involving crowd dynamics 35 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 36 Waymo has created a multi agent simulation environment Carcraft to test algorithms for self driving cars 37 38 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 See also editComparison of agent based modeling software Agent based computational economics ACE Artificial brain Artificial intelligence Artificial life Artificial philosophy AI mayor Black box Blackboard system Complex systems Discrete event simulation Distributed artificial intelligence Emergence Evolutionary computation Friendly artificial intelligence Game theory Hallucination artificial intelligence Human based genetic algorithm Hybrid intelligent system Knowledge Query and Manipulation Language KQML Microbial intelligence Multi agent planning Multi agent reinforcement learning Pattern oriented modeling PlatBox Project Reinforcement learning Scientific community metaphor Self reconfiguring modular robot Simulated reality Social simulation Software agent Software bot Swarm intelligence Swarm roboticsReferences edit Hu J Bhowmick P Jang I Arvin F Lanzon A A Decentralized Cluster Formation Containment Framework for Multirobot Systems IEEE Transactions on Robotics 2021 Hu J Turgut A Lennox B Arvin F Robust Formation Coordination of Robot Swarms with Nonlinear Dynamics and Unknown Disturbances Design and Experiments IEEE Transactions on Circuits and Systems II Express Briefs 2021 Hu J Bhowmick P Lanzon A Group Coordinated Control of Networked Mobile Robots with Applications to Object Transportation IEEE Transactions on Vehicular Technology 2021 Wiering M A 2000 Multi agent reinforcement learning for traffic light control Machine Learning Proceedings of the Seventeenth International Conference Icml 2000 1151 1158 hdl 1874 20827 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 S2CID 17934527 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 10 1 1 65 4539 doi 10 1145 1255438 1255441 S2CID 207163424 Archived from the original on April 2 2010 Retrieved March 18 2008 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 Archived from the original PDF on June 3 2013 Retrieved April 28 2012 a href Template Cite journal html title Template Cite journal cite journal a Cite journal requires journal help Genc Zulkuf et al 2013 Agent Based Information Infrastructure for Disaster Management PDF Intelligent Systems for Crisis Management Lecture Notes in Geoinformation and Cartography pp 349 355 doi 10 1007 978 3 642 33218 0 26 ISBN 978 3 642 33217 3 Hu Junyan Bhowmick Parijat Lanzon Alexander 2020 Distributed Adaptive Time Varying Group Formation Tracking for Multiagent Systems With Multiple Leaders on Directed Graphs IEEE Transactions on Control of Network Systems 7 140 150 doi 10 1109 TCNS 2019 2913619 S2CID 149609966 Sun Ron Naveh Isaac June 30 2004 Simulating Organizational Decision Making Using a Cognitively Realistic Agent Model Journal of Artificial Societies and Social Simulation a b Kubera Yoann Mathieu Philippe Picault Sebastien 2010 Everything can be Agent PDF Proceedings of the Ninth International Joint Conference on Autonomous Agents and Multi Agent Systems AAMAS 2010 1547 1548 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 Salamon Tomas 2011 Design of Agent Based Models Repin Bruckner Publishing p 22 ISBN 978 80 904661 1 1 Weyns Danny Omicini Amdrea Odell James 2007 Environment as a first class abstraction in multiagent systems Autonomous Agents and Multi Agent Systems 14 1 5 30 CiteSeerX 10 1 1 154 4480 doi 10 1007 s10458 006 0012 0 S2CID 13347050 Wooldridge Michael 2002 An Introduction to MultiAgent Systems John Wiley amp Sons p 366 ISBN 978 0 471 49691 5 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 10 1 1 307 6671 doi 10 1007 s10458 005 2631 2 S2CID 19706 The Multi Agent Systems Lab University of Massachusetts Amherst Retrieved October 16 2009 Albrecht Stefano Stone Peter 2017 Multiagent Learning Foundations and Recent Trends Tutorial IJCAI 17 conference PDF Cucker Felipe Steve Smale 2007 The Mathematics of Emergence PDF Japanese Journal of Mathematics 2 197 227 doi 10 1007 s11537 007 0647 x S2CID 2637067 Retrieved June 9 2008 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 S2CID 14655317 Retrieved June 9 2008 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 S2CID 2734931 Yang Lidong Li Zhang 2021 Motion control in magnetic microrobotics From individual and multiple robots to swarms Annual Review of Control Robotics and Autonomous Systems 4 509 534 doi 10 1146 annurev control 032720 104318 S2CID 228892228 Pinan Basualdo Franco Misra Sarthak 2023 Collaborative Magnetic Agents for 3D Microrobotic Grasping Advanced Intelligent Systems doi 10 1002 aisy 202300365 S2CID 262167298 OMG Document orbos 97 10 05 Update of Revised MAF Submission www omg org Retrieved February 19 2019 Ahmed Salman Karsiti Mohd N Agustiawan Herman 2007 A development framework for collaborative robots using feedback control CiteSeerX 10 1 1 98 879 a href Template Cite journal html title Template Cite journal cite journal a Cite journal requires journal help IEEE IES Technical Committee on Industrial Agents TC IA tcia ieee ies org Retrieved February 19 2019 Leitao Paulo Karnouskos Stamatis March 26 2015 Industrial agents emerging applications of software agents in industry Leitao Paulo Karnouskos Stamatis Amsterdam Netherlands ISBN 978 0128003411 OCLC 905853947 a href Template Cite book html title Template Cite book cite book a CS1 maint location missing publisher link Film showcase MASSIVE Retrieved April 28 2012 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 S2CID 579475 Xiao Feng Xie S Smith G Barlow Schedule driven coordination for real time traffic network control International Conference on Automated Planning and Scheduling ICAPS Sao Paulo Brazil 2012 323 331 Mahr 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 10 1 1 153 770 doi 10 1016 j trc 2009 04 018 Generation Expansion Planning Considering Investment Dynamic of Market Participants Using Multi agent System IEEE Conference Publication December 17 2019 doi 10 1109 SGC 2018 8777904 S2CID 199058301 a href Template Cite journal html title Template Cite journal cite journal a Cite journal requires journal help Distributed Multi Agent System Based Load Frequency Control for Multi Area Power System in Smart Grid IEEE Journals amp Magazine December 17 2019 doi 10 1109 TIE 2017 2668983 S2CID 31816181 a href Template Cite journal html title Template Cite journal cite journal a Cite journal requires journal help a b AI can predict your future behaviour with powerful new simulations New Scientist Gong Xiaoqian Herty Michael Piccoli Benedetto Visconti Giuseppe May 3 2023 Crowd Dynamics Modeling and Control of Multiagent Systems Annual Review of Control Robotics and Autonomous Systems 6 1 261 282 doi 10 1146 annurev control 060822 123629 ISSN 2573 5144 Hallerbach S Xia Y Eberle U Koester F 2018 Simulation Based Identification of Critical Scenarios for Cooperative and Automated Vehicles SAE International Journal of Connected and Automated Vehicles SAE International 1 2 93 doi 10 4271 2018 01 1066 Madrigal Story by Alexis C Inside Waymo s Secret World for Training Self Driving Cars The Atlantic Retrieved August 14 2020 Connors J Graham S Mailloux L 2018 Cyber Synthetic Modeling for Vehicle to Vehicle Applications In International Conference on Cyber Warfare and Security Academic Conferences International Limited 594 XI Further reading editWooldridge Michael 2002 An Introduction to MultiAgent Systems John Wiley amp Sons p 366 ISBN 978 0 471 49691 5 Shoham Yoav Leyton Brown Kevin 2008 Multiagent Systems Algorithmic Game Theoretic and Logical Foundations Cambridge University Press p 496 ISBN 978 0 521 89943 7 Mamadou Tadiou Kone Shimazu A Nakajima T August 2000 The State of the Art in Agent Communication Languages ACL Knowledge and Information Systems 2 2 1 26 Hewitt Carl Inman Jeff November December 1991 DAI Betwixt and Between From Intelligent Agents to Open Systems Science 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 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 ISBN 978 0 262 23203 6 Ferber Jacques 1999 Multi Agent Systems An Introduction to Artificial Intelligence Addison Wesley ISBN 978 0 201 36048 6 Weyns Danny 2010 Architecture Based Design of Multi Agent Systems Springer ISBN 978 3 642 01063 7 Sun Ron 2006 Cognition and Multi Agent Interaction Cambridge University Press ISBN 978 0 521 83964 8 Keil David Goldin Dina 2006 Weyns Danny Parunak Van Michel Fabien eds Indirect Interaction in Environments for Multiagent Systems LNCS 3830 Vol 3830 Springer pp 68 87 doi 10 1007 11678809 5 ISBN 978 3 540 32614 4 a href Template Cite book html title Template Cite book cite book a journal ignored help Whitestein Series in Software Agent Technologies and Autonomic Computing published by Springer Science Business Media Group Salamon Tomas 2011 Design of Agent Based Models Developing Computer Simulations for a Better Understanding of Social Processes Bruckner Publishing ISBN 978 80 904661 1 1 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 Fasli Maria 2007 Agent technology for E commerce John Wiley amp Sons p 480 ISBN 978 0 470 03030 1 Cao Longbing Gorodetsky Vladimir Mitkas Pericles A 2009 Agent Mining The Synergy of Agents and Data Mining IEEE Intelligent Systems vol 24 no 3 64 72 Retrieved from https en wikipedia org w index php title Multi agent system amp oldid 1180993336, wikipedia, wiki, book, books, library,

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