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Agent Based Modeling and Simulation

  • Reference work entry
Computational Complexity

Article Outline

Glossary

Definition of the Subject

Introduction

Agent‐Based Models for Simulation

Platforms for Agent‐Based Simulation

Future Directions

Bibliography

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Notes

  1. 1.

    http://www.ima.umn.edu/complex/fall/agent.html

  2. 2.

    With this expression we mean pieces of software which are not designed to interact with agents and agent based systems.

  3. 3.

    http://ccl.northwestern.edu/netlogo/

  4. 4.

    http://repast.sourceforge.net/

  5. 5.

    http://www.simsesam.de/

Abbreviations

Agent:

The definition of the term agent is controversial even inside the restricted community of computer scientists dealing with research on agent models and technologies [25]. A weak definition, that could be suited to describe the extremely heterogeneous approaches in the agent‐based simulation context, is “an autonomous entity, having the ability to decide the actions to be carried out in the environment and interactions to be established with other agents, according to its perceptions and internal state”.

Agent architecture:

The term agent architecture [53] refers to the internal structure that is responsible of effectively selecting the actions to be carried out, according to the perceptions and internal state of an agent. Different architectures have been proposed in order to obtain specific agent behaviors and they are generally classified into deliberative and reactive (respectively, hysteretic and tropistic, according to the classification reported in [29]).

Autonomy:

The term autonomy has different meanings, for it represents (in addition to the control of an agent over its own internal state) different aspects of the possibility of an agent to decide about its own actions. For instance, it may represent the possibility of an agent to decide (i) about the timing of an action, (ii) whether or not to fulfill a request, (iii) to act without the need of an external trigger event (also called pro‐activeness or proactivity) or even (iv) basing on its personal experience instead of hard-wired knowledge [53]. It must be noted that different agent models do not generally embody all the above notions of autonomy.

Interaction:

“An interaction occurs when two or more agents are brought into a dynamic relationship through a set of reciprocal actions” [22].

Environment:

“The environment is a first-class abstraction that provides the surrounding conditions for agents to exist and that mediates both the interaction among agents and the access to resources” [66].

Platform for agent‐based simulation:

a software framework specifically aimed at supporting the realization of agent‐based simulation systems; this kind of framework often provides abstractions and mechanisms for the definition of agents and their environments, to support their interaction, but also additional functionalities like the management of the simulation (e. g. set-up, configuration, turn management), its visualization, monitoring and the acquisition of data about the simulated dynamics.

Bibliography

  1. Adami C (1998) Introduction to Artificial Life. Springer, NewYork

    Book  MATH  Google Scholar 

  2. Agha G (1986) Actors: A Model of Concurrent Computation in DistributedSystems.MIT press, Cambridge

    Google Scholar 

  3. Alfi V, Galla T, Marsili M, Pietronero L (eds) (2007) Interacting Agents,Complexity and Inter-Disciplinary Applications (IACIA)

    Google Scholar 

  4. Balmer M, Nagel K (2006) Shape morphing of intersection layouts using curb sideoriented driver simulation.In: van Leeuwen JP, Timmermans HJ (eds) Innovations in Design & Decision Support Systems in Architecture and UrbanPlanning. Springer, Dordrecht, pp 167–183

    Chapter  Google Scholar 

  5. Bandini S, Manzoni S, Simone C (2002) Heterogeneous agents situated inheterogeneous spaces.Appl Artific Intell 16:831–852

    Article  Google Scholar 

  6. Bandini S, Manzoni S, Vizzari G (2004) Situated cellular agents: a model tosimulate crowding dynamics.IEICE Transactions on Information and Systems: Special Issues on Cellular Automata E87-D,pp 669–676

    Google Scholar 

  7. Bandini S, Petta P, Vizzari G (eds) (2006) International Symposium on AgentBased Modeling and Simulation (ABModSim 2006), vol Cybernetics and Systems. Austrian Society for Cybernetic Studies (2006) 18th European Meeting onCybernetics and Systems Research (EMCSR 2006)

    Google Scholar 

  8. Bandini S, Celada F, Manzoni S, Puzone R, Vizzari G (2006) Modelling the immunesystem with situated agents.In: Apolloni B, Marinaro M, Nicosia G, Tagliaferri R (eds) Proceedings of WIRN/NAIS 2005. Lecture Notes inComputer Science, vol 3931. Springer, Berlin, pp 231–243

    Google Scholar 

  9. Bandini S, Federici ML, Vizzari G (2007) Situated cellular agents approach tocrowd modeling and simulation. Cybernet Syst 38:729–753

    Article  MATH  Google Scholar 

  10. Bar-Yam Y (1997) Dynamics of Complex Systems. Addison-Wesley,Reading

    MATH  Google Scholar 

  11. Batty M (2001) Agent based pedestrian modeling.Env Plan B: Plan Des28:321–326

    Article  Google Scholar 

  12. Bazzan ALC, Wahle J, Klügl F (1999) Agents in trafficmodelling – from reactive to social behaviour.In: Burgard W, Christaller T, Cremers AB (eds) KI-99: Advances in Artificial Intelligence, 23rdAnnual German Conference on Artificial Intelligence, Bonn, Germany, 13–15 September 1999.Lecture Notes in Computer Science,vol 1701. Springer, Berlin, pp 303–306

    Google Scholar 

  13. Brooks RA (1986) A robust layered control system for a mobile robot.IEEE J Robot Autom 2:14–23

    Article  MathSciNet  Google Scholar 

  14. Brooks RA (1990) Elephants don't play chess.Robot Autonom Syst6:3–15

    Article  Google Scholar 

  15. Brueckner S (2000) An analytic approach to pheromone-based coordination.In:ICMAS IEEE Comp Soc, pp 369–370

    Google Scholar 

  16. Cabri G, Leonardi L, Zambonelli F (2000) MARS: a programmablecoordination architecture for mobile agents.IEEE Inter Comp 4:26–35

    Article  Google Scholar 

  17. Davidsson P, Logan B, Takadama K (eds) (2005) Multi-Agent andMulti-Agent-Based Simulation, Joint Workshop MABS (2004), New York, 19 July 2004, Revised Selected Papers.In: Davidsson P, Logan B, Takadama K (eds)MABS. Lecture Notes in Computer Science, vol 3415. Springer, Berlin

    Google Scholar 

  18. Dosi G, Fagiolo G, Roventini A (2006) An evolutionary model of endogenousbusiness cycles.Comput Econ 27:3–34

    Article  MATH  Google Scholar 

  19. Edmonds B (2001) The use of models – making MABS more informative.In: Multi-Agent-Based Simulation, Second International Workshop MABS (2000), Boston MA, USA, July (2000), Revised and Additional Papers. Lecture Notes inComputer Science, vol 1979. Springer, Berlin, pp 15–32

    Google Scholar 

  20. Englemore RS, Morgan T (eds) (1988) Blackboard Systems.Addison-Wesley,Reading

    Google Scholar 

  21. Epstein JM, Axtell R (1996) Growing Artificial Societies.MIT Press,Boston

    Google Scholar 

  22. Ferber J (1999) Multi–Agent Systems. Addison-Wesley,London

    Google Scholar 

  23. Ferber J, Muller J (1996) Influences and reaction: A model of situatedmultiagent systems.In: Proceedings of the 2nd International Conference on Multiagent Systems

    Google Scholar 

  24. Fikes RE, Nilsson NJ (1971) STRIPS: a new approach to the application oftheorem proving to problem solving.Artif Intell 2:189–208

    Article  MATH  Google Scholar 

  25. Franklin S, Graesser A (1997) Is it an agent, or justa program?: A taxonomy for autonomous agents.In: Müller JP, Wooldridge M, Jennings NR (eds) Intelligent Agents III, Agent Theories,Architectures, and Languages ECAI '96 Workshop (ATAL), Budapest, 12–13 August 1996. Lecture Notes in Computer Science, vol 1193. Springer,Berlin, pp 21–36

    Google Scholar 

  26. Gelernter D (1985) Generative communication in Linda.ACM Trans Program LangSyst 7:80–112

    Article  MATH  Google Scholar 

  27. Gelernter D, Carriero N (1992) Coordination languages and their significance.Commun ACM 35:97–107

    Article  Google Scholar 

  28. Genesereth MR, Ketchpel SP (1994) Software agents.Commun ACM37(7):48–53

    Article  Google Scholar 

  29. Genesereth MR, Nilsson N (1987) Logical Foundations of ArtificialIntelligence.Morgan Kaufmann, San Mateo

    MATH  Google Scholar 

  30. Georgeff M (1984) A theory of action in multi–agent planning.In:Proceedings of the AAAI84, pp 121–125

    Google Scholar 

  31. Gilbert N, Troitzsch KG (2005) Simulation for the Social Scientist 2ndedn. Open University Press, Maidenhead

    Google Scholar 

  32. Goles E, Martinez S (1990) Neural and automata networks, dynamical behaviorand applications. Kluwer, Norwell

    Book  MATH  Google Scholar 

  33. Gouaich A, Michel F, Guiraud Y (2005) MIC: A deployment environment forautonomous agents.In: Environments for Multi-Agent Systems, First International Workshop (E4MAS 2004). Lecture Notes in Computer Science,vol 3374. Springer, Berlin, pp 109–126

    Google Scholar 

  34. Gruber TR (1995) Toward principles for the design of ontologies used forknowledge sharing.Int J Hum Comp Stud 43:907–928

    Article  Google Scholar 

  35. Hales D, Edmonds B, Norling E, Rouchier J (eds) (2003)Multi-Agent-Based Simulation III, 4th International Workshop MABS (2003), Melbourne, 14 July 2003. Revised Papers.In: Hales D, Edmonds B,Norling E, Rouchier J (eds) MABS. Lecture Notes in Computer Science, vol 2927. Springer, Berlin

    Google Scholar 

  36. Hassas S, Serugendo GDM, Phan D (eds) (2007) Multi-Agents for modellingComplex Systems (MA4CS). http://bat710.univ-lyon1.fr/~farmetta/MA4CS07

  37. Helbing D, Schweitzer F, Keltsch J, Molnár P (1997) Active walker modelfor the formation of human and animal trail systems.Phys Rev E 56:2527–2539

    Google Scholar 

  38. Henein CM, White T (2005) Agent-based modelling of forces in crowds.In:Davidsson P, Logan B, Takadama K (eds) Multi-Agent and Multi-Agent-Based Simulation, Joint Workshop MABS (2004), New York 19 July 2004. Revised SelectedPapers. Lecture Notes in Computer Science, vol 3415. Springer, Berlin, pp 173–184

    Google Scholar 

  39. Klügl F, Herrler R, Oechslein C (2003) From simulated to realenvironments: How to use sesam for software development.In: Schillo M, Klusch M, Müller JP, Tianfield H (eds) MATES. Lecture Notes in ComputerScience, vol 2831. Springer, Berlin, pp 13–24

    Google Scholar 

  40. Klügl F, Fehler M, Herrler R (2005) About the role of the environment inmulti-agent simulations.In: Weyns D, Parunak HVD, Michel F (eds) Environments for Multi-Agent Systems, First International Workshop E4MAS (2004), NewYork 19 July 2004. Revised Selected Papers. vol 3374, pp 127–149

    Google Scholar 

  41. Langton C (1995) Artificial life: An overview. MIT Press,Cambridge

    Google Scholar 

  42. Latombe JC (1991) Robot Motion Planning.Kluwer,Boston

    Book  Google Scholar 

  43. Luck M, McBurney P, Sheory O, Willmott S (eds) (2005) Agent Technology:Computing as Interaction.University of Southampton, Southampton

    Google Scholar 

  44. Mamei M, Zambonelli F, Leonardi L (2002) Co-fields: towards a unifyingapproach to the engineering of swarm intelligent systems.In: Engineering Societies in the Agents World III: Third International Workshop (ESAW2002). Lecture Notes in Artificial Intelligence, vol 2577. Springer, Berlin, pp 68–81

    Google Scholar 

  45. Moss S, Davidsson P (eds) (2001) Multi-Agent-Based Simulation,Second International Workshop MABS (2000), Boston, July, (2000), Revised and Additional Papers. Lecture Notes in Computer Science,vol 1979. Springer, Berlin

    Google Scholar 

  46. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE77:541–580

    Article  Google Scholar 

  47. North MJ, Collier NT, Vos JR (2006) Experiences creating three implementationsof the repast agent modeling toolkit.ACM Trans Model Comp Sim 16:1–25

    Article  Google Scholar 

  48. Omicini A, Zambonelli F (1999) Coordination for Internet applicationdevelopment.Autono Agents Multi-Agent Syst 2:251–269 Special Issue: Coordination Mechanisms for Web Agents

    Article  Google Scholar 

  49. Picco GP, Murphy AL, Roman GC (1999) Lime: Linda meets mobility.In:Proceedings of the 21st International Conference on Software Engineering (ICSE 99) ACM Press, Los Angeles,pp 368–377

    Google Scholar 

  50. Pyka A, Fagiolo G (2007) Agent-Based Modelling: A Methodology forNeo-Schumpeterian Economics. In: Hanusch H, Pyka A (eds) Elgar Companion to Neo-Schumpeterian Economics. Edward Elgar Publishing,pp 467–487

    Google Scholar 

  51. Rao A, Georgeff M (1991) Modeling rational agents withina BDI‐architecture.In: Proc Knowledge Representation and Reasoning (KR&R 1991)

    Google Scholar 

  52. Rao A, Georgeff M (1995) BDI agents: from theory to practice.In: Proceedingsof the International Conference on Multi-Agent Systems

    Google Scholar 

  53. Russel S, Norvig P (1995) Artificial Intelligence: A Modern Approach.Prentice Hall, Upper Saddle River

    Google Scholar 

  54. Schadschneider A, Kirchner A, Nishinari K (2002) CA approach to collectivephenomena in pedestrian dynamics.In: Bandini S, Chopard B, Tomassini M (eds) Cellular Automata, 5th International Conference on Cellular Automata forResearch and Industry ACRI 2002. Lecture Notes in Computer Science, vol 2493. Springer, Berlin, pp 239–248

    Google Scholar 

  55. Sichman JS, Antunes L (eds) (2006) Multi-Agent-Based Simulation VI,International Workshop MABS (2005), Utrecht, The Netherlands, 25 July 2005, Revised and Invited Papers.In: Sichman JS, Antunes L (eds)MABS. Lecture Notes in Computer Science, vol 3891. Springer, Berlin

    Google Scholar 

  56. Sichman JS, Conte R, Gilbert N (eds) (1998) Multi-Agent Systemsand Agent-Based Simulation, First International Workshop MABS '98, Paris, France, 4–6 July 1998. Proceedings.In: Sichman JS, Conte R,Gilbert N (eds): MABS. Lecture Notes in Computer Science, vol 1534. Springer, Berlin

    Google Scholar 

  57. Sichman JS, Bousquet F, Davidsson P (eds) (2003)Multi-Agent-Based Simulation, Third International Workshop MABS (2002), Bologna, Italy, 15–16 July 2002, Revised Papers.In: Sichman JS,Bousquet F, Davidsson P (eds) MABS. Lecture Notes in Computer Science, vol 2581. Springer, Berlin

    Google Scholar 

  58. Torrens P (2002) Cellular automata and multi-agent systems as planning supporttools. In: Geertman S, Stillwell J (eds) Planning Support Systems in Practice. Springer, London, pp 205–222

    Google Scholar 

  59. Tummolini L, Castelfranchi C, Ricci A, Viroli M, Omicini A (2004)“Exhibitionists” and “voyeurs” do it better: A shared environment approach for flexible coordination with tacit messages.In: Weyns D, Parunak HVD, Michel F (eds) 1st International Workshop on Environments for MultiAgent Systems (E4MAS 2004),pp 97–111

    Google Scholar 

  60. Wahle J, Schreckenberg M (2001) A multi-agent system for on-linesimulations based on real-world traffic data.In: Annual Hawaii International Conference on System Sciences (HICSS-34), IEEE ComputerSociety

    Google Scholar 

  61. Weyns D, Holvoet T (2006) From reactive robots to situated multi-agentsystems: a historical perspective on the role of environment in multi-agent systems.In: Dikenelli O, Gleizes MP, Ricci A (eds) EngineeringSocieties in the Agents World VI, 6th International Workshop ESAW (2005). Lecture Notes in Computer Science, vol 3963. Springer, Berlin,pp 63–88

    Chapter  Google Scholar 

  62. Weyns D, Schelfthout K, Holvoet T, Lefever T (2005) Decentralized control ofE'GV transportation systems.In: AAMAS Industrial Applications. ACM Press, Utrecht, pp 67–74

    Google Scholar 

  63. Weyns D, Vizzari G, Holvoet T (2006) Environments for situated multi-agentsystems: beyond infrastructure.In: Weyns D, Parunak HVD, Michel F (eds) Environments for Multi-Agent Systems II, Second International Workshop E4MAS(2005), Utrecht, 25 July 2005.Selected Revised and Invited Papers. Lecture Notes in Computer Science, vol 3830. Springer, Berlin, pp 1–17

    Google Scholar 

  64. Weyns D, Boucké N, Holvoet T (2006) Gradient field-based task assignmentin an AGV transportation system.In: AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems. ACMPress, Hakodate, pp 842–849

    Google Scholar 

  65. Weyns D, Brueckner SA, Demazeau Y (eds) (2008) EngineeringEnvironment-Mediated Multi-Agent Systems: International Workshop, EEMMAS 2007, Dresden, Germany, October 2007. Selected Revised and InvitedPapers. Lecture Notes in Computer Science, vol 5049. Springer, Berlin

    Google Scholar 

  66. Weyns D, Omicini A, Odell J (2007) Environment as a first classabstraction in multiagent systems.Auton Agents Multi-Agent Syst 14:5–30

    Article  Google Scholar 

  67. Wolfram S (1986) Theory and Applications of Cellular Automata.World Press,Singapore

    MATH  Google Scholar 

  68. Wooldridge MJ, Jennings NR (1995) Intelligent agents: theory and practice.Knowl Eng Rev 10:115–152

    Article  Google Scholar 

  69. Zambonelli F, Parunak HVD (2003) Signs of a revolution in computerscience and software engineering. In: Petta P, Tolksdorf R, Zambonelli F (eds) Engineering Societies in the Agents World III, Third InternationalWorkshop, ESAW 2002, Madrid, Spain, September 2002, Revised Papers. Lecture Notes in Computer Science, vol 2577. Springer, Berlin,pp 13–28

    Google Scholar 

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Authors and Affiliations

  1. Complex Systems and Artificial Intelligence Research Center, University of Milan-Bicocca, Milan, Italy

    Stefania Bandini, Sara Manzoni & Giuseppe Vizzari

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  1. Stefania Bandini
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  2. Sara Manzoni
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  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Bandini, S., Manzoni, S., Vizzari, G. (2012). Agent Based Modeling and Simulation. In: Meyers, R. (eds) Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_7

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