Skip to main content

The Complexity of Finding an Optimal Policy for Language Convergence

  • Conference paper
From Animals to Animats 9 (SAB 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4095))

Included in the following conference series:

Abstract

An important problem for societies of natural and artificial animals is to converge upon a similar language in order to communicate. We call this the language convergence problem. In this paper we study the complexity of finding the optimal (in terms of time to convergence) algorithm for language convergence. We map the language convergence problem to instances of a Decentralized Partially Observable Markov Decision Process to show that the complexity can vary from P-complete to NEXP-complete based on the scenario being studied.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Clark, A.: Magic words: How language augments human computation. In: Carruthers, P., Boucher, J. (eds.) Language And Thought: Interdisciplinary Themes. Cambridge University Press, Cambridge (1998)

    Google Scholar 

  2. Cucker, F., Smale, S., Zhou, D.X.: Modeling language evolution. Foundations of Computational Mathematics 4(3), 315–343 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  3. Steels, L.: The origins of ontologies and communication conventions in multi-agent systems. Autonomous Agents and Multi-Agent Systems 1(2), 169–194 (1998)

    Article  Google Scholar 

  4. Bernstein, D.S., Givan, R., Immerman, N., Zilberstein, S.: The complexity of decentralized control of markov decision processes. Math. Oper. Res. 27(4), 819–840 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  5. Goldman, C., Zilberstein, S.: Decentralized control of cooperative systems: Categorization and complexity analysis. Journal of Artificial Intelligence Research 22, 143–174 (2004)

    MATH  MathSciNet  Google Scholar 

  6. Steels, L.: The evolution of communication systems by adaptive agents. In: Alonso, E., Kudenko, D., Kazakov, D. (eds.) AAMAS 2000 and AAMAS 2002. LNCS (LNAI), vol. 2636, pp. 125–140. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  7. Wagner, K., Reggia, J., Uriagereka, J., Wilkinson, G.: Progress in the simulation of emergent communication and language. Adaptive Behavior 11, 37–69 (2003)

    Article  Google Scholar 

  8. Komarova, N.L.: Replicator-mutator equation, universality property and population dynamics of learning. Journal of Theoretical Biology 230(2), 227–239 (2004)

    Article  MathSciNet  Google Scholar 

  9. Delgado, J.: Emergence of social conventions in complex networks. Artificial Intelligence 141(1-2), 171–185 (2002)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lakkaraju, K., Gasser, L. (2006). The Complexity of Finding an Optimal Policy for Language Convergence. In: Nolfi, S., et al. From Animals to Animats 9. SAB 2006. Lecture Notes in Computer Science(), vol 4095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840541_66

Download citation

  • DOI: https://doi.org/10.1007/11840541_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38608-7

  • Online ISBN: 978-3-540-38615-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics