Rens, GMeyer, TCasini, G2017-01-162017-01-162016-08Rens, G., Meyer, T. and Casini, G. 2016. On revision of partially specified convex probabilistic belief bases. In: European Conference on Artificial Intelligence (ECAI), 31 August - 2 September 2016, Holland.http://www.cair.za.net/research/outputs/revision-partially-specified-convex-probabilistic-belief-baseshttp://hdl.handle.net/10204/8899European Conference on Artificial Intelligence (ECAI), 31 August - 2 September 2016, Holland.Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website.We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results.enArtificial intelligenceAIProbabilistic beliefsArticleRens, G., Meyer, T., & Casini, G. (2016). On revision of partially specified convex probabilistic belief bases. http://hdl.handle.net/10204/8899Rens, G, T Meyer, and G Casini "On revision of partially specified convex probabilistic belief bases." (2016) http://hdl.handle.net/10204/8899Rens G, Meyer T, Casini G. On revision of partially specified convex probabilistic belief bases. 2016; http://hdl.handle.net/10204/8899.TY - Article AU - Rens, G AU - Meyer, T AU - Casini, G AB - We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results. DA - 2016-08 DB - ResearchSpace DP - CSIR KW - Artificial intelligence KW - AI KW - Probabilistic beliefs LK - https://researchspace.csir.co.za PY - 2016 T1 - On revision of partially specified convex probabilistic belief bases TI - On revision of partially specified convex probabilistic belief bases UR - http://hdl.handle.net/10204/8899 ER -