Belief revision through the belief-function formalism in a multi-agent environment
replacing the “priority to the incoming information principle” with the “recoverability principle”: any previously believed piece of information must belong to the current cognitive state whenever it is possible
dealing not just with pieces of information but with couples <source, information> since the reliability of the source affects the credibility of the information and vice-versa.
The “belief-function” formalism is here accepted as a simple and intuitive way to transfer the sources' reliability to the information's credibility.
Unable to display preview. Download preview PDF.
- Kinny, D. and Georgeff, M., Modelling and Design of Multi-Agent Systems, in this VolumeGoogle Scholar
- 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
- P. Gärdenfors, Knowledge in Flux: Modeling the Dynamics of Epistemic States, Cambridge, Mass., MIT Press, 1988.Google Scholar
- P. Gärdenfors, Belief Revision, Cambridge University Press, 1992.Google Scholar
- 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
- 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
- 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
- Katsuno H. and Mendelzon A.O., On the difference between updating a knowledge base and revising it, in Allen J., Fikes R. and Sandewall E. (eds.), Proc. of the 2nd Inter. Conf. on Principles of Knowledge Representation and Reasoning, Morgan Kaufmann, pp. 387–394, 1991.Google Scholar
- Chou Tymothy S.-C. and Winslett M., A Model-Based Belief Revision System, in Journal of Automated Reasoning, 12, pp. 157–208, Kluwer Academic Publishers, 1994.Google Scholar
- 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
- Pearl J., Probabilistic Reasoning for Intelligent Systems, Morgan Kaufmann Publishers, 1988.Google Scholar
- Dubois D. and Prade H., Belief Change and Possibility Theory, in Gärdenfors P. (eds.), Belief Revision, Cambridge University Press, 1992.Google Scholar
- Shafer G., Belief Functions, in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, Morgan Kaufmann Publishers, 1990.Google Scholar
- Rott, H., A Nonmonotonic Conditional Logic for Belief Revision I, in A. Fuhrmann and M. Morreau (eds.), The Logic of Theory Change, Springer-Verlag, LNAI 465, Berlin, 135–183,1991.Google Scholar
- R. Reiter, A Theory of Diagnosis from First Principles, in Artificial Intelligence, 53, 1987.Google Scholar
- 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