Learning agents' reliability through Bayesian Conditioning: A simulation experiment

  • Aldo Franco Dragoni
  • Paolo Giorgini
Learning About/From Other Agents and the World
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1221)


This paper reports the first results of a simulation experiment. There are two databases, one containing true propositions and the other containing their respective negations. Five agents in turn access one of them. Each agent has a “capacity” that will be used as the frequency with which the agent accesses (unconsciously) the database with the correct knowledge. Agents randomly exchange information with the others. Since they have limited degrees of capacity, their “cognitive state” quickly becomes inconsistent. Each agent is equipped with the same belief revision mechanism to detect and solve these contradictions. This adopts the Dempster's Rule of Combination to evaluate the credibility of the various pieces of information and Bayesian Conditioning to estimate the relative degrees of reliability of the agents (itself included). The purpose of the experiments was that of evaluating on a statistical basis, the emergent cognitive behavior of the group.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    A.F. Dragoni, P. Giorgini, “Belief Revision through the Belief Function Formalism in a Multi-Agent Environment”, Third International Workshop on Agent Theories, Architectures and Languages, LNAI Series, Springer-Verlag, 1997.Google Scholar
  2. [2]
    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
  3. [3]
    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
  4. [4]
    P. Gärdenfors, Knowledge in Flux: Modeling the Dynamics of Epistemic States, Cambridge, Mass., MIT Press, 1988.Google Scholar
  5. [5]
    P. Gärdenfors, Belief Revision, Cambridge University Press, 1992.Google Scholar
  6. [6]
    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
  7. [7]
    W. Nebel, Base Revision Operations and Schemes: Semantics, Representation, and Complexity, in Colin A.G. (eds.), Proc. of the 11th European Conference on Artificial Intelligence, John Wiley & Sons, 1994.Google Scholar
  8. [8]
    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
  9. [9]
    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
  10. [10]
    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
  11. [11]
    de Kleer J., An Assumption Based Truth Maintenance System, in Artificial Intelligence, 28, pp. 127–162, 1986.Google Scholar
  12. [12]
    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
  13. [13]
    Shafer G. (1976), A Mathematical Theory of Evidence, Princeton University Press, Princeton, New Jersey.Google Scholar
  14. [14]
    Shafer G., Belief Functions, in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, Morgan Kaufmann Publishers, 1990.Google Scholar
  15. [15]
    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
  16. [16]
    Dragoni A.F., Belief Revision: from theory to practice, to appear on “The Knowledge Engineering Review”, Cambridge University Press, 1997.Google Scholar
  17. [17]
    A.F. Dragoni, P. Giorgini and P. Puliti, Distributed Belief Revision vs. Distributed Truth Maintenance, in Proc. 6th IEEE Conf. on fools with A.I., IEEE Computer Press, 1994.Google Scholar
  18. [18]
    R. Reiter, A Theory of Diagnosis from First Principles, in Artificial Intelligence, 53, 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Aldo Franco Dragoni
    • 1
  • Paolo Giorgini
    • 1
  1. 1.Istituto di InformaticaUniversità di AnconaAnconaItaly

Personalised recommendations