Preview
Unable to display preview. Download preview PDF.
References
HSIA Y.-T. (1990). The belief calculus and uncertain reasoning. In Proceedings of the Eighth National Conference on Artificial Intelligence, American Association for Artificial Intelligence, Boston, Massachusetts, 120–125.
HSIA Y.-T. (1991a). Characterizing belief with minimum commitment. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, Sydney, Australia (to appear).
HSIA Y.-T. (1991b). Belief and surprise — a belief-function approach. In Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference (B.D. D'Ambrosio, P. Smets, and P.P. Bonissone, eds.), Morgan Kaufman, San Mateo, California (to appear).
HSIA Y.-T. (1991c). A belief-function semantics for cautious nonmonotonicity. Technical Report TR/IRIDIA/91-3, IRIDIA, Université Libre de Bruxelles, Brussels.
HSIA Y.-T. and SHENOY, P.P. (1989). An evidential language for expert systems. In Proceedings of the Fourth International Symposium on Methodology for Intelligent Systems, Charlotte, N.C., 9–16.
KENNES R. (1991) Computatioanl aspects of the Moebius transform of a graph. IEEE-SMC, under press.
KENNES R. and SMETS P. (1990a) Computational Aspects of the Möbius Transform. Uncertainty in Artificial Intelligence Vol. 6 (P.P. Bonissone, M. Henrion, L.N. Kanal, J.F. Lemmer, eds.), North-Holland, Amsterdam, to appear.
KENNES R. and SMETS P. (1990b) Fast algorithms for Dempster-Shafer theory. Proc. of the 3rd Inter. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Paris, France, July 2–6, 1990, 51–55. To appear in (B. Bouchon-Meunier, R.R. Yager, L.A. Zadeh, eds.), Lecture Notes in Computer Science Series, Springer Verlag.
LASKEY K. B. and LEHNER P. E. (1989). Assumptions, beliefs and probabilities. Artificial Intelligence, 41, 1, 65–77.
PEARL J. (1990) Reasoning with Belief Functions: an Analysis of Compatibility. Intern. J. Approx. Reasoning. 4:363–390.
SAFFIOTTI A. (1990a). A Hybrid Framework for Representing Uncertain Knowledge”. Procs. of the Eighth AAAI Conference (Boston, MA) 653–658.
SAFFIOTTI A. (1990b). “Using Dempster-Shafer Theory in Knowledge Representation”. Procs. of the Sixth Conf. on Uncertainty in AI (Cambridge, MA) 352–359.
SAFFIOTTI A. (1991). “Inference-Driven Construction of Valuation Systems”. IRIDIA Technical Report (In preparation).
SAFFIOTTI A. and UMKEHRER E. (1991) “Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks”. Procs. of the 7th Conference on Uncertainty in AI.
SHAFER G. (1976) A mathematical theory of evidence. Princeton Univ. Press. Princeton, NJ.
SHENOY P.P. and SHAFER G. (1988) “An Axiomatic Framework for Bayesian and Belief-Function Propagation”. Procs. of AAAI Workshop on Uncertainty in AI: 307–314.
SMETS Ph. (1978) Un modéle mathématico-statistique simulant le processus du diagnostic médical. Doctoral dissertation. Université Libre de Bruxelles, Bruxelles, (Available through University Microfilm International, 30–32 Mortimer Street, London WIN 7RA, thesis 80-70,003).
SMETS Ph. (1986) Bayes' theorem generalized for belief functions. Proc. ECAI-86, vol II., 169–171. ECCAI.
SMETS Ph. (1988) Belief functions, in SMETS Ph, MAMDANI A., DUBOIS D. and PRADE H. ed. Non standard logics for automated reasoning. Academic Press, London p 253–286.
SMETS Ph. (1990a) Varieties of ignorance. To appear in Information Sciences.
SMETS Ph. (1990b) The transferable belief model and random sets. To appear in Int. J. Intell. Systems.
SMETS Ph. (1990c) The Transferable Belief Model and Other Interpretations of Dempster-Shafer's Model. Uncertainty in Artificial Intelligence Vol. 6 (P.P. Bonissone, M. Henrion, L.N. Kanal, J.F. Lemmer, eds.), North-Holland, Amsterdam, to appear.
SMETS Ph. (1990d) The transferable belief model and possibility theory. Proc. NAFIPS-90, pg. 215–218.
SMETS Ph. (1990e) Constructing the pignistic probability function in a context of uncertainty. Uncertainty in Artificial Intelligence 5, Henrion M., Shachter R.D., Kanal L.N. and Lemmer J.F. eds, North Holland, Amsterdam., 29–40.
SMETS Ph. (1990f) The combination of evidence in the transferable belief model. IEEE-Pattern analysis and Machine Intelligence, 12:447–458.
SMETS Ph. (1991a) Belief induced by the knowledge of some probabilities. Technical Report: TR-IRIDIA-91-9
SMETS Ph. (1991b) Resolving misunderstandings about belief functions: A response to the many criticisms raised by J. Pearl. To appear in Int. J. Approximate Reasoning.
SMETS Ph. (1991c) Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. (submitted for publication)
SMETS Ph. and HSIA Y.T. (1990) Default reasoning and the transferable belief model. Uncertainty in Artificial Intelligence Vol. 6 (P.P. Bonissone, M. Henrion, L.N. Kanal, J.F. Lemmer, eds.), North-Holland, Amsterdam, to appear.
SMETS Ph. and KENNES (1990) The transferable belief model. Technical Report: TR-IRIDIA-90-14.
Xu, H. (1991) “An Efficient Implementation of Belief Function Propagation”. Procs. of the 7th Conference on Uncertainty in AI (to appear).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Smets, P., Hsia, Y.T., Saffiotti, A., Kennes, R., Xu, H., Umkehren, E. (1991). The transferable belief model. In: Kruse, R., Siegel, P. (eds) Symbolic and Quantitative Approaches to Uncertainty. ECSQARU 1991. Lecture Notes in Computer Science, vol 548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54659-6_72
Download citation
DOI: https://doi.org/10.1007/3-540-54659-6_72
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-54659-7
Online ISBN: 978-3-540-46426-6
eBook Packages: Springer Book Archive