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Self-emergence of Lexicon Consensus in a Population of Autonomous Agents by Means of Evolutionary Strategies

  • Darío Maravall
  • Javier de Lope
  • Raúl Domínguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

In Multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of evolutionary strategies to the self-emergence of a common lexicon in a population of agents. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the agents or the states of the environment itself) into symbols or signals we check whether it is possible for the population to converge in an autonomous, decentralized way to a common lexicon, so that the communication efficiency of the entire population is optimal. We have conducted several experiments, from the simplest case of a 2×2 association matrix (i.e. two meanings and two symbols) to a 3×3 lexicon case and in both cases we have attained convergence to the optimal communication system by means of evolutionary strategies. To analyze the convergence of the population of agents we have defined the population’s consensus when all the agents (i.e. the 100% of the population) share the same association matrix or lexicon. As a general conclusion we have shown that evolutionary strategies are powerful enough optimizers to guarantee the convergence to lexicon consensus in a population of autonomous agents.

Keywords

Multi-agent systems Evolution of artificial languages Computational semiotics Evolutionary strategies Self-collective coordination Evolutionary language games Signaling games 

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References

  1. 1.
    de Saussure, F.: Cours de Linguistic Général, Payot, Paris (1916); Ibidem Course on General Linguistics. English Edn. McGraw-Hill, New York (1969)Google Scholar
  2. 2.
    Peirce, C.S.: Selected Writings. Dover, New York (1966)Google Scholar
  3. 3.
    Nowak, M.A.: The evolutionary language game. J. Theor. Biol. 200, 147–162 (1999)CrossRefGoogle Scholar
  4. 4.
    Steels, L., Kaplan, F.: Bootstrapping grounded word semantics. In: Briscoe, T. (ed.) Linguistic Evolution Through Language Acquisition, pp. 53–73. Cambridge University Press, Cambridge (2002)CrossRefGoogle Scholar
  5. 5.
    Lenaerts, T., Jansen, B., Tuyls, K., et al.: The evolutionary language game: An orthogonal Approach. J. Theor. Biol. 235, 566–582 (2005)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Lewis, D.K.: Convention. Harvard University Press, Cambridge (1969)Google Scholar
  7. 7.
    Duro, R.J., Graña, M., de Lope, J.: On the potential contributions of hybrid intelligent approaches to multicomponent robotic system development. Information Sciences (in press) (2010)Google Scholar
  8. 8.
    Oliphant, M.: Formal approaches to innate and learned communication: Laying the foundation for language. Ph.D. Thesis Diss. U. of California, San Diego (1997)Google Scholar
  9. 9.
    Kaplan, F.: Simple models of distributed coordination. Connection Science 17(3-4), 249–270 (2005)CrossRefGoogle Scholar
  10. 10.
    Divina, F., Vogt, P.: A hybrid model for learning word-meaning mappings. In: Vogt, P., Sugita, Y., Tuci, E., Nehaniv, C.L. (eds.) EELC 2006. LNCS (LNAI), vol. 4211, pp. 1–15. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Darío Maravall
    • 1
    • 2
  • Javier de Lope
    • 2
  • Raúl Domínguez
    • 2
  1. 1.Dept. of Artificial Intelligence, Faculty of Computer ScienceUniversidad Politécnica de Madrid 
  2. 2.Centro de Automática y Robótica (UPM – CSIC)Universidad Politécnica de Madrid 

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