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Compiling min-based possibilistic causal networks: a mutilated-based approach. (English) Zbl 1341.68236
Liu, Weiru (ed.), Symbolic and quantitative approaches to reasoning with uncertainty. 11th European conference, ECSQARU 2011, Belfast, UK, June 29 – July 1, 2011. Proceedings. Berlin: Springer (ISBN 978-3-642-22151-4/pbk). Lecture Notes in Computer Science 6717. Lecture Notes in Artificial Intelligence, 700-712 (2011).
Summary: Qualitative causal possibilistic networks are important tools for handling uncertain information in the possibility theory framework. Despite their importance, no compilation has been performed to ensure causal reasoning in possibility theory framework. This paper proposes two compilation-based inference algorithms for min-based possibilistic causal networks. The first is a possibilistic adaptation of the probabilistic inference method [8] and the second is a purely possibilistic approach. Both of them are based on an encoding of the network into a propositional theory and a compilation of this output in order to efficiently compute the effect of both observations and interventions, while adopting a mutilation strategy.
For the entire collection see [Zbl 1216.68033].

68T37 Reasoning under uncertainty in the context of artificial intelligence
Full Text: DOI
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