Advertisement

KI - Künstliche Intelligenz

, Volume 27, Issue 3, pp 273–280 | Cite as

Learning Tools for Agent-Based Modeling and Simulation

  • Robert Junges
  • Franziska Klügl
Research Project

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.

Keywords

Agent-based simulation Agent modeling Agent learning 

References

  1. 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. 2.
    Alonso E, D’Inverno M, Kudenko D, Luck M, Noble J (2001) Learning in multi-agent systems. Knowl Eng Rev 16:277–284 CrossRefGoogle Scholar
  3. 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 CrossRefGoogle Scholar
  4. 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 CrossRefGoogle Scholar
  5. 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. 6.
    Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Natural computing series. Springer, Berlin zbMATHCrossRefGoogle Scholar
  7. 7.
    Epstein JM (2011) Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton Google Scholar
  8. 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 CrossRefGoogle Scholar
  9. 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. 10.
    Goldstein J (1999) Emergence as a construct: history and issues. Emergence 1(1):49–72 CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. 12.
    Hester T, Stone P (2012) Texplore: real-time sample-efficient reinforcement learning for robots. Mach Learn 1–45 Google Scholar
  13. 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 Google Scholar
  14. 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 CrossRefGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. 16.
    Junges R, Klügl F (2012) Programming agent behavior by learning in simulation models. Appl Artif Intell 26(4):349–375 CrossRefGoogle Scholar
  17. 17.
    Klügl F (2009) Agent-based simulation engineering. Habilitation Thesis, University of Würzburg, unpublished. Available online via the homepage of the author Google Scholar
  18. 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. 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 CrossRefGoogle Scholar
  20. 20.
    Koza J (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87–112 CrossRefGoogle Scholar
  21. 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 CrossRefGoogle Scholar
  22. 22.
    Law AM, Kelton DW (2007) Simulation modelling and analysis. McGraw-Hill, New York Google Scholar
  23. 23.
    Railsback SF, Grimm V (2012) Agent-based and individual-based modeling—a practical introduction. Princeton University Press, Princeton Google Scholar
  24. 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. 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. 26.
    Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge Google Scholar
  27. 27.
    Watkins CJCH, Dayan P (1992) Q-learning. Mach Learn 8(3):279–292 zbMATHGoogle Scholar
  28. 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 CrossRefGoogle Scholar
  29. 29.
    Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149–175 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Örebro UniversityÖrebroSweden

Personalised recommendations