Dempster, A. P. (1968), A generalization of Bayesian inference, Journal of Royal Statistical Society, Series B, 30, 205–247.

Dubois, D. and Prade, H. (1988), Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Publishing Company, New York.

Dubois, D. and Prade, H. (1990), Epistemic entrenchment and possibilistic logic, unpublished manuscript.

Gardenfors, P. (1988), Knowledge in Flux: Modeling the Dynamics of Epistemic States, MIT Press, Cambridge, MA.

Geiger, D. and Pearl, J. (1990), On the logic of causal models, Uncertainty in Artificial Intelligence 4, Shachter, R., Levitt, T., Lemmer, J and Kanal, L., eds., 3–14, North-Holland.

Ginsberg, M. L. (1984), Non-monotonic reasoning using Dempster's rule, Proceedings of the Third National Conference on Artificial Intelligence (AAAI-84), 126–129, Austin, TX.

Hunter, D. (1988), Graphoids, semi-graphoids, and ordinal conditional functions, unpublished manuscript.

Hunter, D. (1990), Parallel belief revision, Uncertainty in Artificial Intelligence 4, Shachter, R., Levitt, T., Lemmer, J and Kanal, L., eds., 241–252, North-Holland.

Jeffrey, R. C. (1983), The Logic of Decision, 2nd edition, Chicago University Press

Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan-Kaufmann.

Pearl, J. (1989). Probabilistic semantics for nonmonotonic reasoning: A survey, Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, Toronto, Canada, pp. 505–516.

Shafer, G. (1976), A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ.

Shafer, G. (1984), The problem of dependent evidence, School of Business Working Paper No. 164, University of Kansas, Lawrence, KS.

Shafer, G. (1987), Belief functions and possibility measures, in Analysis of Fuzzy Information, volume I: Mathematics and Logic, Bezdek, J. C. (ed.), 51–84, CRC Press.

Shenoy, P. P. (1989), A valuation-based language for expert systems,

International Journal of Approximate Reasoning,

3(5), 359–416.

CrossRefShenoy, P. P. (1989b), On Spohn's rule for revision of beliefs, School of Business Working Paper No. 213, University of Kansas, Lawrence, KS. To appear in International Journal of Approximate Reasoning in 1991.

Shenoy, P. P. and Shafer, G. (1988), An axiomatic framework for Bayesian and belief-function propagation, Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence, 307–314, Minneapolis, MN.

Shenoy, P. P. and Shafer, G. (1990), Axioms for probability and belief-function propagation, Uncertainty in Artificial Intelligence 4, Shachter, R., Levitt, T., Lemmer, J and Kanal, L., eds., 169–198, North-Holland.

Spohn, W. (1988), Ordinal conditional functions: A dynamic theory of epistemic states, in Harper, W. L. and Skyrms, B. (eds.), Causation in Decision, Belief Change, and Statistics, II, 105–134, D. Reidel Publishing Company.

Spohn, W. (1990), A general non-probabilistic theory of inductive reasoning, Uncertainty in Artificial Intelligence 4, Shachter, R., Levitt, T., Lemmer, J and Kanal, L., eds., 149–158, North-Holland.

Verma, T. and Pearl, J. (1990), Causal networks: Semantics and expressiveness, Uncertainty in Artificial Intelligence 4, Shachter, R., Levitt, T., Lemmer, J and Kanal, L., eds., 69–78, North-Holland.

Zadeh, L. A. (1978), Fuzzy sets as a basis for a theory of possibility,

Fuzzy Sets and Systems,

1, 3–28.

CrossRef