Skip to main content
SpringerLink
Log in

Learning Tools for Agent-Based Modeling and Simulation

  • Research Project
  • Published:
KI - Künstliche Intelligenz Aims and scope Submit manuscript

Abstract

In this project report, we describe ongoing research on supporting the development of agent-based simulation models. The vision is that the agents themselves should learn their (individual) behavior model, instead of letting a human modeler test which of the many possible agent-level behaviors leads to the correct macro-level observations. To that aim, we integrate a suite of agent learning tools into SeSAm, a fully visual platform for agent-based simulation models. This integration is the focus of this contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://en.wikipedia.org/wiki/Comparison_of_agent-based_modeling_software.

  2. http://repast.sourceforge.net/.

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

References

  1. Allahyari H, Lavesson N (2011) User-oriented assessment of classification model understandability. In: Eleventh Scandinavian conference on artificial intelligence SCAI 2011, vol 227. IOS Press, Amsterdam, pp 11–19

    Google Scholar 

  2. Alonso E, D’Inverno M, Kudenko D, Luck M, Noble J (2001) Learning in multi-agent systems. Knowl Eng Rev 16:277–284

    Article  Google Scholar 

  3. Bernon C, Gleizes MP, Peyruqueou S, Picard G (2003) Adelfe: a methodology for adaptive multi-agent systems engineering. In: Petta P, Tolksdorf R, Zambonelli F (eds) Engineering societies in the agents world III. LNCS, vol 2577. Springer, Berlin, pp 70–81

    Chapter  Google Scholar 

  4. Busoniu L, Babuska R, De Schutter B (2008) A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst Man Cybern, Part C, Appl Rev 38(2):156–172

    Article  Google Scholar 

  5. Deisenroth MP, Rasmussen CE (2011) Pilco: a model-based and data-efficient approach to policy search. In: Getoor L, Scheffer T (eds) Proceedings of the 28th international conference on machine learning, ICML 2011. Omnipress, Madison, pp 465–472

    Google Scholar 

  6. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Natural computing series. Springer, Berlin

    Book  MATH  Google Scholar 

  7. Epstein JM (2011) Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton

    Google Scholar 

  8. Garro A, Russo W (2010) Easyabms: a domain-expert oriented methodology for agent-based modeling and simulation. Simul Model Pract Theory 18(10):1453–1467

    Article  Google Scholar 

  9. Ghorbani A, Bots P, Dignum V, Dijkema G (2013) Maia: a framework for developing agent-based social simulations. J Artif Soc Soc Simul 16(2):9

    Google Scholar 

  10. Goldstein J (1999) Emergence as a construct: history and issues. Emergence 1(1):49–72

    Article  Google Scholar 

  11. Gomez-Sanz JJ, Fernandez CR, Arroyo J (2010) Model driven development and simulation with the INGENIAS agent framework. Simul Model Pract Theory 18:1468–1482

    Article  Google Scholar 

  12. Hester T, Stone P (2012) Texplore: real-time sample-efficient reinforcement learning for robots. Mach Learn 1–45

  13. Huysmans J, Baesens B, Vanthienen J (2006) Using rule extraction to improve the comprehensibility of predictive models. Open access publications from Katholieke Universiteit Leuven, Katholieke Universiteit Leuven

  14. Junges R, Klugl F (2012) Behavior abstraction robustness in agent modeling. In: 2012 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology (WI-IAT), vol 2, pp 228–235

    Chapter  Google Scholar 

  15. Junges R, Klugl F (2012) How to design agent-based simulation models using agent learning. In: Proceedings of the 2012 Winter simulation conference (WSC), pp 1–10

    Chapter  Google Scholar 

  16. Junges R, Klügl F (2012) Programming agent behavior by learning in simulation models. Appl Artif Intell 26(4):349–375

    Article  Google Scholar 

  17. Klügl F (2009) Agent-based simulation engineering. Habilitation Thesis, University of Würzburg, unpublished. Available online via the homepage of the author

  18. Klügl F (2009) SeSAm: visual programming and participatory simulation for agent-based models. Uhrmacher AM, Weyns D (eds) Multi-agent systems: simulation and applications. Taylor & Francis, London. Chap. 16

    Google Scholar 

  19. Klügl F, Herrler R, Fehler M (2006) Sesam: implementation of agent-based simulation using visual programming. In: Proceedings of the fifth international joint conference on autonomous agents and multiagent systems AAMAS ’06, pp 1439–1440

    Chapter  Google Scholar 

  20. Koza J (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87–112

    Article  Google Scholar 

  21. Kubera Y, Mathieu P, Picault S (2011) Ioda: an interaction-oriented approach for multi-agent based simulations. Auton Agents Multi-Agent Syst 23(3):303–343

    Article  Google Scholar 

  22. Law AM, Kelton DW (2007) Simulation modelling and analysis. McGraw-Hill, New York

    Google Scholar 

  23. Railsback SF, Grimm V (2012) Agent-based and individual-based modeling—a practical introduction. Princeton University Press, Princeton

    Google Scholar 

  24. Richiardi M, Leombruni R, Saam NJ, Sonnessa M (2006) A common protocol for agent-based social simulation. J Artif Soc Soc Simul 9(1):15

    Google Scholar 

  25. Schneider JG (1996) Exploiting model uncertainty estimates for safe dynamic control learning. In: Mozer M, Jordan MI, Petsche T (eds) Advances in neural information processing systems, vol 9. MIT Press, Cambridge, pp 1047–1053

    Google Scholar 

  26. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge

    Google Scholar 

  27. Watkins CJCH, Dayan P (1992) Q-learning. Mach Learn 8(3):279–292

    MATH  Google Scholar 

  28. WeißG (1996) Adaptation and learning in multi-agent systems: some remarks and a bibliography. In: IJCAI ’95: proceedings of the workshop on adaption and learning in multi-agent systems. Springer, London, pp 1–21

    Chapter  Google Scholar 

  29. Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Örebro University, 70182, Örebro, Sweden

    Robert Junges & Franziska Klügl

Authors
  1. Robert Junges
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Franziska Klügl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert Junges.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Junges, R., Klügl, F. Learning Tools for Agent-Based Modeling and Simulation. Künstl Intell 27, 273–280 (2013). https://doi.org/10.1007/s13218-013-0258-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13218-013-0258-z

Keywords

Search

Navigation