How the World Was MADE: Parametrization of Evolved Agent-Based Models for Backstory Generation

  • Rubén H. García-OrtegaEmail author
  • Pablo García-Sánchez
  • J. J. Merelo
  • María Isabel G. Arenas
  • Pedro A. Castillo
  • Antonio M. Mora
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)


Generating fiction environments for a multi-agent system optimized by genetic algorithms (with some specific requirements related to the desirable plots), presents two main problems: first it is impossible to know in advance the optimal value for the particular designed fitness function, and at the same time, it creates a vast search space for the parameters that it needs. The purpose of this paper is to define a methodology to find the best parameter values for both, the evolutionary algorithm, and the own fictional world configuration. This design includes running, to completion, a world simulation represented as a chromosome, and assigning a fitness to it, thus composing a very complex fitness landscape.

In order to optimize the resources allocated to evolution and to have some guarantees that the final result will be close to the optimum, we systematically analyze a set of possible values of the most relevant parameters, obtaining a set of generic rules. These rules, when applied to the plot requisites, and thus, to the fitness function, will lead to a reduced range of parameter values that will help the storyteller to create optimal worlds with a reduced computation budget.


Games Plot Content generation Evolutionary algorithms Agent based models 



This work has been supported in part by SIPESCA (Programa Operativo FEDER de Andalucía 2007–2013), TIN2011-28627-C04-02 (Spanish Ministry of Economy and Competitivity), SPIP2014-01437 (Dirección General de Tráfico), PRY142/14 (Fundación Pública Andaluza Centro de Estudios Andaluces en la IX Convocatoria de Proyectos de Investigación) and PYR-2014-17 GENIL project (CEI-BIOTIC Granada).


  1. 1.
    García-Ortega, R.H., García-Sánchez, P., Mora, A.M., Merelo, J.: My life as a sim: evolving unique and engaging life stories using virtual worlds. In: ALIFE 14: The Fourteenth Conference on the Synthesis and Simulation of Living Systems, vol. 14, pp. 580–587 (2014)Google Scholar
  2. 2.
    Nairat, M., Dahlstedt, P., Nordahl, M.G.: Character evolution approach to generative storytelling. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, pp. 1258–1263. IEEE, New Orleans, LA, USA, 5–8 June 2011Google Scholar
  3. 3.
    Booth, T.L.: Sequential Machines and Automata Theory, 1st edn. Wiley, New York (1967)zbMATHGoogle Scholar
  4. 4.
    Togelius, J., Yannakakis, G.N., Stanley, K.O., Browne, C.: Search-based procedural content generation: a taxonomy and survey. IEEE Trans. Comput. Intell. AI Games 3(3), 172–186 (2011)CrossRefGoogle Scholar
  5. 5.
    Arinbjarnar, M., Barber, H., Kudenko, D.: A critical review of interactive drama systems. In: Abu-Shaaban, Y. (ed.) AISB 2009 Symposium, pp. 15–26. AI & Games, Edinburgh (2009)Google Scholar
  6. 6.
    Meehan, J.R.: The metanovel: writing stories by computer. Technical report, DTIC Document (1976).
  7. 7.
    Lebowitz, M.: Story-telling as planning and learning. Poetics 14(6), 483–502 (1985)CrossRefGoogle Scholar
  8. 8.
    Turner, S.R.: The Creative Process: A computer Model of Storytelling and Creativity. Psychology Press, New York (2014)Google Scholar
  9. 9.
    Riedl, M.O., Leon, C.: Toward vignette-based story generation for drama management systems. In: Barber, H., Thue, D., eds.: Proceedings of the 2nd international conference on intelligent technologies for interactive entertainment (INTETAIN), workshop on integrating technologies for interactive stories, pp. 23–28 (2008)Google Scholar
  10. 10.
    Garry, J., El-Shamy, H.: Archetypes and Motifs in Folklore and Literature. M.E Sharpe, New York (2005)Google Scholar
  11. 11.
    Swartjes, I., Vromen, J.: Emergent story generation: Lessons from improvisational theater. In: Magerko, B.S., Riedl, M.O. (eds.) Intelligent Narrative Technologies: Papers from the AAAI Fall Symposium. Number FS-07-05 in AAAI Fall Symposium Series, pp. 146–149 (2007)Google Scholar
  12. 12.
    Cioffi-Revilla, C., De Jong, K., Bassett, J.K.: Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems. Comput. Math. Organ. Theory 18(3), 356–373 (2012)CrossRefGoogle Scholar
  13. 13.
    Morrell, J.: Between the Lines: Master the Subtle Elements of Fiction Writing. Writer’s Digest Books, Cincinnati (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Rubén H. García-Ortega
    • 1
    Email author
  • Pablo García-Sánchez
    • 1
  • J. J. Merelo
    • 1
  • María Isabel G. Arenas
    • 1
  • Pedro A. Castillo
    • 1
  • Antonio M. Mora
    • 1
  1. 1.Department of Computer Architecture and TechnologyUniversity of GranadaGranadaSpain

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