Skip to main content

Distributed belief revision vs. belief revision in a multi-agent environment: First results of a simulation experiment

  • Papers
  • Conference paper
  • First Online:
Multi-Agent Rationality (MAAMAW 1997)

Abstract

We propose a distributed architecture for belief revision-integration, where each element is conceived as a complex system able to exchange opinions with the others. Since nodes can be affected by some degree of incompetence, part of the information running through the network may be incorrect. Incorrect information may cause contradictions in the knowledge base of some nodes. To manage these contradictions, each node is equipped with a belief revision module which makes it able to discriminate among more or less credible information and more or less reliable information sources. Our aim is that of comparing on a simulation basis the performances and the characteristics of this distributed system vs. those of a centralised architecture. We report here the first results of our experiments.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mason, C., An Intelligent Assistant for Nuclear Test Ban Treaty Verification, IEEE Expert, vol 10, no 6, 1995.

    Google Scholar 

  2. Cindy L. Mason and Rowland R. Johnson, Datms: A Framework for Distributed Assumption Based Reasoning, in L. Gasser and M. N. Huhns eds., Distributed Artificial Intelligence 2, Pitman/Morgan Kaufmann, London, pp 293–318, 1989.

    Google Scholar 

  3. Huhns, M. N., Bridgeland, D. M.: Distributed Truth Maintenance. In Dean, S. M., editor, Cooperating Knowledge Based Systems, pages 133–147. Springer-Verlag, 1990.

    Google Scholar 

  4. de Kleer J., An Assumption Based Truth Maintenance System, in Artificial Intelligence, 28, pp. 127–162, 1986.

    Google Scholar 

  5. Dragoni A.F., A Model for Belief Revision in a Multi-Agent Environment, in Werner E. and Demazeau Y. (eds.), Decentralized A. I. 3, North Holland Elsevier Science Publisher, 1992.

    Google Scholar 

  6. Dragoni A.F., Belief Revision: from theory to practice, to appear on “The Knowledge Engineering Review”, Cambridge University Press, 1997.

    Google Scholar 

  7. Dragoni A.F., Ceresi, C. and Pasquali, V., A System to Support Complex Inquiries, in Proc. of the “V Congreso Iberoamericano de Derecho e Informatica”, La Habana, 6–11 march 1996.

    Google Scholar 

  8. A.F. Dragoni, P. Giorgini and P. Puliti, Distributed Belief Revision vs. Distributed Truth Maintenance, in Proc. 6th IEEE Conf. on Tools with A.I., IEEE Computer Press, 1994.

    Google Scholar 

  9. A.F. Dragoni, P. Giorgini, “Belief Revision through the Belief Function Formalism in a Multi-Agent Environment”, Intelligent Agents III, LNAI nℴ 1193, Springer-Verlag, 1997.

    Google Scholar 

  10. Dragoni, A.F., Maximal Consistency, Theory of Evidence and Bayesian Conditioning in the Investigative Domain, to appear on the “International Journal on Artificial Intelligence and Law”, 1997.

    Google Scholar 

  11. Alchourrón C.E., GÄrdenfors P., and Makinson D., On the Logic of Theory Change: Partial meet Contraction and Revision Functions, in The Journal of Simbolic Logic, 50, pp. 510–530, 1985.

    Google Scholar 

  12. P. GÄrdenfors, Knowledge in Flux: Modeling the Dynamics of Epistemic States, Cambridge, Mass., MIT Press, 1988.

    Google Scholar 

  13. P. GÄrdenfors, Belief Revision, Cambridge University Press, 1992.

    Google Scholar 

  14. W. Nebel, Base Revision Operations and Schemes: Semantics, Representation, and Complexity, in Cohn A.G. (eds.), Proc. of the 11th European Conference on Artificial Intelligence, John Wiley & Sons, 1994.

    Google Scholar 

  15. Benferhat S., Cayrol C., Dubois D., Lang J. and Prade H., Inconsistency Management and Prioritized Syntax-Based Entailment, in Proc. of the 13th Inter. Joint Conf. on Artificial Intelligence, pp. 640–645, 1993.

    Google Scholar 

  16. Dubois D. and Prade H., A Survey of Belief Revision and Update Rules in Various Uncertainty Models, in International Journal of Intelligent Systems, 9, pp. 61–100, 1994.

    Google Scholar 

  17. Williams M.A., Iterated Theory Base Change: A Computational Model, in Proc. of the 14th Inter. Joint Conf. on Artificial Intelligence, pp. 1541–1547, 1995.

    Google Scholar 

  18. Dragoni A.F., Mascaretti F. and Puliti P., A Generalized Approach to Consistency-Based Belief Revision, in Gori, M. and Soda, G. (Eds.), Topics in Artificial Intelligence, LNAI 992, Springer Verlag, 1995.

    Google Scholar 

  19. Martins J.P. and Shapiro S.C. (1988), A Model for Belief Revision, in «Artificial Intelligence», 35, pp. 25–79.

    Google Scholar 

  20. R. Reiter, A Theory of Diagnosis from First Principles, in Artificial Intelligence, 53, 1987.

    Google Scholar 

  21. Benferhat S., Dubois D. and Prade H., How to infer from inconsistent beliefs without revising?, in Proc. of the 14th Inter. Joint Conf. on Artificial Intelligence, pp. 1449–1455, 1995.

    Google Scholar 

  22. Shafer G. and Srivastava R., The Bayesian and Belief-Function Formalisms a General Perpsective for Auditing, in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, Morgan Kaufmann Publishers, 1990.

    Google Scholar 

  23. Shafer G. (1976), A Mathematical Theory of Evidence, Princeton University Press, Princeton, New Jersey.

    Google Scholar 

  24. Shafer G., Belief Functions, in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, Morgan Kaufmann Publishers, 1990.

    Google Scholar 

  25. Kennes, R., Computational Aspects of the Möbius Transform of a Graph, IEEE Transactions in Systems, Man and Cybernetics, 22, pp 201–223, 1992.

    Google Scholar 

  26. Parson, S., Some qualitative approaches to applying the Demster-Shafer theory, Information and Decision Technologies, 19, pp 321–337, 1994.

    Google Scholar 

  27. Moral, S. and Wilson, N., Importance Sampling Monte-Carlo Algorithms for the Calculation of Dempster-Shafer Belief, Proceeding of IPMU'96, Granada, 1996.

    Google Scholar 

  28. A.F. Dragoni, P. Giorgini, “Learning Agents' Reliability through Bayesian Conditioning: a simulation study”, in Weiss (ed.) “Learning in DAI Systems”, LNAI nℴ, Springer-Verlag, 1997.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Magnus Boman Walter Van de Velde

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dragoni, A.F., Giorgini, P., Baffetti, M. (1997). Distributed belief revision vs. belief revision in a multi-agent environment: First results of a simulation experiment. In: Boman, M., Van de Velde, W. (eds) Multi-Agent Rationality. MAAMAW 1997. Lecture Notes in Computer Science, vol 1237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63077-5_25

Download citation

  • DOI: https://doi.org/10.1007/3-540-63077-5_25

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63077-7

  • Online ISBN: 978-3-540-69125-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics