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Conditioning in decomposable compositional models in valuation-based systems. (English) Zbl 1252.68310

Greco, Salvatore (ed.) et al., Advances in computational intelligence. 14th international conference on information processing and management of uncertainty in knowledge-based systems, IPMU 2012, Catania, Italy, July 9–13, 2012. Proceedings, Part IV. Berlin: Springer (ISBN 978-3-642-31723-1/pbk; 978-3-642-31724-8/ebook). Communications in Computer and Information Science 300, 676-685 (2012).
Summary: Valuation-based systems (VBS) can be considered as a generic uncertainty framework that has many uncertainty calculi, such as probability theory, a version of possibility theory where combination is the product t-norm, Spohn’s epistemic belief theory, and Dempster-Shafer belief function theory, as special cases. In this paper, we focus our attention on conditioning, which is defined using the combination, marginalization, and removal operators of VBS. We show that conditioning can be expressed using the composition operator. We define decomposable compositional models in the VBS framework. Finally, we show that conditioning in decomposable compositional models can be done using local computation. Since all results are obtained in the VBS framework, they hold in all calculi that fit in the VBS framework.
For the entire collection see [Zbl 1251.68018].

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence
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