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Cross-Population Semiosis in Multi-agent Systems

  • Wojciech LorkiewiczEmail author
  • Radosław Katarzyniak
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 430)

Abstract

Semiosis mechanism in an artificial system should guarantee the development of a consistent and common substances of language symbols. Thus allowing a population of interacting agents to autonomously learn, adapt and optimise their semantics. In this research we define a settings for the cross-population semiosis model, which involves two or more mature populations set together in a common environment with a goal to align their predefined (or differently developed) lexicons. In particular, using the language game model we introduce a new type of language game scenario and define a set of specific measures that capture the dynamics of lexicon evolution in cross-population semiosis model. Finally we provide an experimental verification of the behaviour of the alignment process of cross-population semiosis, as such test the applicability of the classical language game model approach.

Keywords

Agent Language game Semiosis process Cognitive semantics 

Notes

Acknowledgements

The publication is financed from the statutory activities of the Faculty of Computer Science and Management of Wroclaw University of Technology.

References

  1. 1.
    Cook, D., Das, S.: How smart are our environments? An updated look at the state of the art. Pervasive and Mobile Computing 3(2), 53–73 (2007)CrossRefGoogle Scholar
  2. 2.
    Hong, X., Nugent, C., Mulvenna, M., McClean, S., Scotney, B., Devlin, S.: Evidential fusion of sensor data for activity recognition in smart homes. Pervasive Mob. Comput. 5(3), 236–252 (2009)CrossRefGoogle Scholar
  3. 3.
    Wark, T., Swain, D., Crossman, C., Valencia, P., Bishop-Hurley, G., Handcock, R.: Sensor and Actuator Networks: Protecting Environmentally Sensitive Areas. IEEE Pervasive Comput. 8(1), 30–36 (2009)CrossRefGoogle Scholar
  4. 4.
    Mirolli, M., Nolfi, S.: Evolving communication in embodied agents: theory, methods, and evaluation. In: Evolution of Communication and Language in Embodied Agents, pp. 105–121 (2010)Google Scholar
  5. 5.
    Rekleitis, I.: Distributed coverage with multi-robot system. In: Proceedings ICRA’06, p. 2423–2429 (2006)Google Scholar
  6. 6.
    Steels, L.: Language as a complex adaptive system. In: Parallel Problem Solving from Nature PPSN VI, pp. 17–26. Springer, Berlin (2000)Google Scholar
  7. 7.
    Steels, L.: Modeling the formation of language in embodied agents: methods and open challenges. In: Evolution of Communication and Language in Embodied Agents, pp. 223–233 (2010)Google Scholar
  8. 8.
    de Saussure, F.: Course in general linguistics La Salle, IL.: Open Court (1983)Google Scholar
  9. 9.
    Lorkiewicz, W., Katarzyniak, R.: Issues on aligning the meaning of symbols in multiagent systems. In: New Challenges in Computational Collective Intelligence, Studies in Computational Intelligence, vol. 244, pp. 217–229. Springer, Berlin (2009)Google Scholar
  10. 10.
    Lorkiewicz, W., Katarzyniak, R., Kowalczyk, R.: Individual semiosis in multi-agent systems. In: Transactions on Computational Collective Intelligence VII (Lecture Notes in Computer Science, vol. 7270) pp. 164–197 (2010)Google Scholar
  11. 11.
    Lorkiewicz, W., Katarzyniak, R.: Multi-stage and multi-participant interaction patterns in multi-agent naming game. Comput. Methods Sci. Technol. 20(2), 59–80 (2014)CrossRefGoogle Scholar
  12. 12.
    Cangelosi, A.: The grounding and sharing of symbols. In: Cognition Distributed: How Cognitive Technology Extends Our Minds, p. 83 (2008)Google Scholar
  13. 13.
    Cangelosi, A., Parisi, D.: Simulating the evolution of language. Springer, NY (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Vogt, P., Coumans, H.: Investigating social interaction strategies for bootstrapping lexicon development. J. Artif. Soc. Soc. Simul. 6(1), 1 (2003)Google Scholar
  15. 15.
    DeVylder, B., Tuyls, K.: Towards a common lexicon in the naming game: the dynamics of synonymy reduction. In: Workshop on Semiotic Dynamics of Language Games (2005)Google Scholar
  16. 16.
    DeBeule, J., DeVylder, B., Belpaeme, T.: A cross-situational learning algorithm for damping homonymy in the guessing game. In: Proceedings of ALIFE X. MIT Press, Cambridge (2006)Google Scholar
  17. 17.
    Lorkiewicz, W., Kowalczyk, R., Katarzyniak, R., Vo Q.B.: On topic selection strategies in multi-agent naming game. In: Proceedings of AAMAS 2011, vol. 2, pp. 499–506 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and ManagementWrocław University of TechnologyWrocławPoland

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