An Axiomatic Framework for Propagating Uncertainty in Directed Acyclic Networks Cano Ocaña, José Enrique Delgado Calvo-Flores, Miguel Moral Callejón, Serafín Causal network Uncertainty Hypertrees PULCINELLA system Marginalization Combination Conditional information Inteligencia artificial Artificial intelligence This paper presents an axiomatic system for propagating uncertainty in Pearl's causal networks, (Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988 [7]). The main objective is to study all aspects of knowledge representation and reasoning in causal networks from an abstract point of view, independent of the particular theory being used to represent information (probabilities, belief functions or upper and lower probabilities). This is achieved by expressing concepts and algorithms in terms of valuations, an abstract mathematical concept representing a piece of information, introduced by Shenoy and Sharer [1, 2]. Three new axioms are added to Shenoy and Shafer's axiomatic framework [1, 2], for the propagation of general valuations in hypertrees. These axioms allow us to address from an abstract point of view concepts such as conditional information (a generalization of conditional probabilities) and give rules relating the decomposition of global information with the concept of independence (a generalization of probability rules allowing the decomposition of a bidimensional distribution with independent marginals in the product of its two marginals). Finally, Pearl's propagation algorithms are also developed and expressed in terms of operations with valuations. 2022-11-10T08:05:09Z 2022-11-10T08:05:09Z 1993-06 journal article José Cano, Miguel Delgado, Serafín Moral, An axiomatic framework for propagating uncertainty in directed acyclic networks, International Journal of Approximate Reasoning, Volume 8, Issue 4, 1993, Pages 253-280, ISSN 0888-613X, [https://doi.org/10.1016/0888-613X(93)90026-A] https://hdl.handle.net/10481/77866 10.1016/0888-613X(93)90026-A eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier