UCT CS Research Document Archive

Revising Incompletely Specified Convex Probabilistic Belief Bases

Rens, Gavin, Giovanni Casini and Thomas Meyer (2016) Revising Incompletely Specified Convex Probabilistic Belief Bases. In Kern-Isberner, Gabriele and Renata Wassermann, Eds. Proceedings Sixteenth International Workshop on Non-Monotonic Reasoning (NMR), pages 133-142, Cape Town, South Africa.

Full text available as:
PDF - Requires Adobe Acrobat Reader or other PDF viewer.


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.

EPrint Type:Conference Paper
Subjects:I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE
ID Code:1117
Deposited By:Meyer, Thomas
Deposited On:29 November 2016