UCT CS Research Document Archive

On Revision of Partially Specified Convex Probabilistic Belief Bases

Rens, Gavin, Thomas Meyer and Giovanni Casini (2016) On Revision of Partially Specified Convex Probabilistic Belief Bases. In Fox, Maria and Gal Kaminka, Eds. Proceedings 22nd European Conference on Artificial Intelligence, pages 921-929, The Hague, Netherlands.

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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.

EPrint Type:Conference Paper
Keywords:Knowledge Representation
Belief Revision
Subjects:I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE
ID Code:1150
Deposited By:Meyer, Thomas
Deposited On:10 Febuary 2017