Skip to main content
SpringerLink
Log in

Dynamic (Dis-)Information in Self-adaptive Distributed Search Systems with Information Delays

  • Conference paper
  • First Online:
Multiagent System Technologies (MATES 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9872))

Included in the following conference series:

  • 566 Accesses

Abstract

This paper studies the effects of self-adaptive change of distributed search systems in which imperfectly informed search agents rather loosely collaborate and pursue objective functions which are not necessarily complements to each other. The results indicate that employing learning-based self-adaptation of major features of search systems may lead to high levels of systems’ performance, although the complexity of the search problem considerably tends to shape the effects of self-adaptation. The results further suggest that the selective effects of self-adaptation correspond to major features of the underlying search problem. This is of particular interest when the structure of the search problem is not known to the designer of the search system.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Basar, T., Olsder, G.J.: Dynamic Noncooperative Game Theory, 2nd edn. Academic Press, New York (1999)

    MATH  Google Scholar 

  2. Baumann, O.: Distributed problem solving in modular systems: the benefit of temporary coordination neglect. Syst. Res. Behav. Sci. 32, 124–136 (2015)

    Article  Google Scholar 

  3. Bush, R.R., Mosteller, F.: Stochastic Models for Learning. Wiley, Oxford (1955). (Engl.)

    Book  MATH  Google Scholar 

  4. Brenner, T.: Agent learning representation: advice on modelling economic learning. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, pp. 895–947. Elsevier, Amsterdam (2006)

    Google Scholar 

  5. Carley, K.M., Gasser, L.: Computational organization theory. In: Weiss, G. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 299–330. MIT Press, Cambridge (1999)

    Google Scholar 

  6. Gross, T., Blasius, B.: Adaptive coevolutionary networks: a review. J. R. Soc. Interface 20, 259–271 (2008)

    Article  Google Scholar 

  7. Hammel, U., Bäck, T.: Evolution strategies on noisy functions how to improve convergence properties. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 159–168. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  8. Hu, J., Zheng, W.X.: Emergent collective behaviors on coopetition networks. Phys. Lett. A 378(2627), 1787–1796 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Google Scholar 

  10. Kauffman, S.A., Levin, S.: Towards a general theory of adaptive walks on rugged landscapes. J. Theor. Biol. 128, 11–45 (1993)

    Article  MathSciNet  Google Scholar 

  11. Kauffman, S.A.: The Origins of Order: Self-organization and Selection in Evolution. Oxford University Press, Oxford (1993)

    Google Scholar 

  12. Law, A.M.: Simulation Modeling and Analysis, 4th edn. McGraw-Hill, New York (2007)

    Google Scholar 

  13. Levitan, B., Kauffman, S.A.: Adaptive walks with noisy fitness measurements. Mol. Divers. 1, 53–68 (1995)

    Article  Google Scholar 

  14. Li, R., Emmerich, M.T.M., Eggermont, J., Bovenkamp, E.G.P., Bäck, T., Dijkstra, J., Reiber, J.H.C.: Mixed-integer NK landscapes. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 42–51. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  15. Reaidy, J., Massotte, P., Diep, D.: Comparison of negotiation protocols in dynamic agent-based manufacturing systems. Int. J. Prod. Econ. 99, 117–130 (2006)

    Article  Google Scholar 

  16. Rivkin, J.W., Siggelkow, N.: Patterned interactions in complex systems: implications for exploration. Manage. Sci. 53, 1068–1085 (2007)

    Article  Google Scholar 

  17. Siggelkow, N., Rivkin, J.W.: Speed and search: designing organizations for turbulence and complexity. Organ. Sci. 16(2), 101–122 (2005)

    Article  Google Scholar 

  18. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. MIT Press, Cambridge (2012). (Mass.)

    Google Scholar 

  19. Wall, F.: The (Beneficial) role of informational imperfections in enhancingorganisational performance. In: Calzi, M.L., Milone, L., Pellizzari, P. (eds.) Progress in Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol. 645, pp. 115–126. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  20. Wall, F.: Beneficial effects of randomized organizational change on performance. Adv. Complex Syst. 18(05n06), 1550019 (2015)

    Article  Google Scholar 

  21. Wall, F.: Agent-based modeling in managerial science: an illustrative survey and study. RMS 10, 135–193 (2016)

    Article  Google Scholar 

  22. Wall, F.: Organizational dynamics in adaptive distributed search processes: effects on performance and the role of complexity. Front. Informa. Technol. Electr. Eng. 17(4), 283–295 (2016)

    Article  Google Scholar 

  23. Welch, B.L.: The significance of the differences between two means when the population variances are unequal. Biometrika 25, 350–362 (1938)

    Article  MATH  Google Scholar 

  24. Yongcan, C., Wenwu, Y., Wei, R., Guanrong, C.: An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans. Ind. Inf. 9, 427–438 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Alpen-Adria-Universitaet Klagenfurt, 9020, Klagenfurt, Austria

    Friederike Wall

Authors
  1. Friederike Wall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Friederike Wall .

Editor information

Editors and Affiliations

  1. DFKI , SaarbrĂĽcken, Germany

    Matthias Klusch

  2. Institute for Computer Science &, University of Duisburg-Essen Institute for Computer Science &, Essen, Nordrhein-Westfalen, Germany

    Rainer Unland

  3. IBM Haifa Research Lab , Haifa, Israel

    Onn Shehory

  4. University of Hamburg VSIS, Hamburg, Hamburg, Germany

    Alexander Pokahr

  5. Distributed Artificial Intelligence, Technical University of Berlin Distributed Artificial Intelligence, Berlin, Berlin, Germany

    Sebastian Ahrndt

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Wall, F. (2016). Dynamic (Dis-)Information in Self-adaptive Distributed Search Systems with Information Delays. In: Klusch, M., Unland, R., Shehory, O., Pokahr, A., Ahrndt, S. (eds) Multiagent System Technologies. MATES 2016. Lecture Notes in Computer Science(), vol 9872. Springer, Cham. https://doi.org/10.1007/978-3-319-45889-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45889-2_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45888-5

  • Online ISBN: 978-3-319-45889-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation