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Modeling Autonomous Adaptive Agents with Functional Language for Simulations

  • Richárd Legéndi
  • László Gulyás
  • Rajmund Bocsi
  • Tamás Máhr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5816)

Abstract

The basic concept of agent-based modeling is to create adaptive agents to operate in a changing environment. Agents make autonomous decisions and modify their environment through continuous interactions. The Functional Agent-Based Language for Simulations (FABLES) is a special purpose language for ABM that is intended to reduce programming skills required to create simulations. The aim of FABLES is to allow modelers to focus on modeling, and not on programming. This paper provides an overview of FABLES, explaining the traits and the design concepts of this hybrid language that merges features of object-oriented, functional and procedural languages to provide flexibility in model design. To demonstrate some of these issues, we describe modeling with FABLES via the popular El Farol Bar problem from a user perspective, by means of example.

Keywords

Functional programming agent-based simulations multi-formalism El Farol Bar problem 

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References

  1. 1.
    Epstein, J.M., Axtell, R.L.: Growing Artificial Societies: Social Science from the Bottom Up, January 1996. MIT Press Books, vol. 1. The MIT Press, Cambridge (1996)Google Scholar
  2. 2.
    Samuelson, D.A., Macal, C.M.: Agent-based Simulation Comes of Age (August 2006)Google Scholar
  3. 3.
    Axelrod, R., Tesfatsion, L.S.: A guide for newcomers to agent-based modeling in the social sciences. Staff General Research Papers 12515, Iowa State University, Department of Economics (March 2006)Google Scholar
  4. 4.
    Tesfatsion, L.S.: Agent-based computational economics: Modeling economies as complex adaptive systems. Staff General Research Papers 12974, Iowa State University, Department of Economics (August 2008)Google Scholar
  5. 5.
    Arthur, W.B.: Inductive Reasoning, Bounded Rationality and the Bar Problem. Working Papers 94-03-014. Santa Fe Institute, New Mexico, USA (1994)Google Scholar
  6. 6.
    North, M.J., Collier, N.T., Vos, J.R.: Experiences Creating Three Implementations of the Repast Agent Modeling Toolkit. ACM Transactions on Modeling and Computer Simulation 16(1), 1–25 (2006)CrossRefGoogle Scholar
  7. 7.
    Minar, N., Burkhart, R., Langton, C., Askenazi, M.: The Swarm simulation system: A toolkit for building multi-agent simulations. Working Paper 96-06-042, Santa Fe Institute, Santa Fe (1996)Google Scholar
  8. 8.
    Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL (1999), http://ccl.northwestern.edu/netlogo/
  9. 9.
    Borshchev, A., Karpov, Y., Kharitonov, V.: Distributed simulation of hybrid systems with AnyLogic and HLA. Future Gener. Comput. Syst. 18(6), 829–839 (2002)CrossRefMATHGoogle Scholar
  10. 10.
    North, M.J., Tatara, E., Collier, N.T., Ozik, J.: Visual Agent-based Model Development with Repast Simphony. In: Proceedings of the Agent 2007 Conference on Complex Interaction and Social Emergence, November 2007, Argonne National Laboratory, Argonne, IL, USA (2007)Google Scholar
  11. 11.
    Parker, M.: MetaABM Agent-based Modeling Software (2009), http://metaabm.org
  12. 12.
    Challet, D., et al.: Shedding light on El Farol. Game Theory and Information 0406002, EconWPA (2004)Google Scholar
  13. 13.
    Challet, D., et al.: Minority Games: Interacting Agents in Financial Markets. Oxford University Press, USA (2005)MATHGoogle Scholar
  14. 14.
    Rand, W., Wilensky, U.: NetLogo El Farol model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. (2007), http://ccl.northwestern.edu/netlogo/models/ElFarol
  15. 15.
    Fogel, D.B., Chellapilla, K., Angeline, P.J.: Inductive reasoning and bounded rationality reconsidered. IEEE Transactions on Evolutionary Computation 3(2), 142–146 (1999)CrossRefGoogle Scholar
  16. 16.
    Whitehead, D.: The El Farol Bar Problem Revisited: Reinforcement Learning in a Potential Game. ESE Discussion Papers 186. Edinburgh School of Economics, University of Edinburgh (2008)Google Scholar
  17. 17.
    Rand, W., Sondahl, F.: The El Farol Bar Problem and Computational Effort: Why People Fail to Use Bars Efficiently. In: Agent 2007, November 15-17, Evanston, IL, USA (2007)Google Scholar
  18. 18.
    Gulyás, L.: On the transition to agent-based modeling: Implementation strategies from variables to agents. Social Science Computer Review 20(4), 389–399 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Richárd Legéndi
    • 1
  • László Gulyás
    • 1
  • Rajmund Bocsi
    • 1
  • Tamás Máhr
    • 1
  1. 1.AITIA International Inc.BudapestHungary

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