On Revision of Partially Specified Convex Probabilistic Belief Bases

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 de- termining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these proba- bility 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 expres- sivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revi- sion employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary dis- tribution method and the optimum entropy methods are reasonable, yet yield different results

Inference for a New Probabilistic Constraint Logic

Contains fulltext : 117051.pdf (preprint version ) (Open Access)IJCAI-13 : Twenty-Third International Joint Conference on Artificial Intelligence Beijing, China, 3–9 August 201