Rens, GMeyer, TCasini, G2016-09-082016-09-082016-04Rens, G. Meyer, T. and Casini, G. 2016. Revising incompletely specified convex probabilistic belief bases. In: Proceedings of the 16th International Workshop on Non-Monotonic Reasoning (NMR), 22-24 April 2016, Cape Town, South Africahttps://arxiv.org/pdf/1604.02133v1.pdfhttp://hdl.handle.net/10204/8766Proceedings of the 16th International Workshop on Non-Monotonic Reasoning (NMR), 22-24 April 2016, Cape Town, South AfricaWe 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. 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.enProbabilistic beliefsPropositional informationLewis ImagingArtificial intelligenceConference PresentationRens, G., Meyer, T., & Casini, G. (2016). Revising incompletely specified convex probabilistic belief bases. Association for the Advancement of Artificial Intelligence. http://hdl.handle.net/10204/8766Rens, G, T Meyer, and G Casini. "Revising incompletely specified convex probabilistic belief bases." (2016): http://hdl.handle.net/10204/8766Rens G, Meyer T, Casini G, Revising incompletely specified convex probabilistic belief bases; Association for the Advancement of Artificial Intelligence; 2016. http://hdl.handle.net/10204/8766 .TY - Conference Presentation 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. 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 - 2016-04 DB - ResearchSpace DP - CSIR KW - Probabilistic beliefs KW - Propositional information KW - Lewis Imaging KW - Artificial intelligence LK - https://researchspace.csir.co.za PY - 2016 T1 - Revising incompletely specified convex probabilistic belief bases TI - Revising incompletely specified convex probabilistic belief bases UR - http://hdl.handle.net/10204/8766 ER -