Rens, Gavin[University of KwaZulu-Natal > School of Mathematics, Statistics and Computational Sciences > > Postdoc]
Meyer, Thomas[University of Cape Town > Department of Computer Science > > Full professor]
Casini, Giovanni[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Apr-2016
Proceedings of the 16th International Workshop on Non-Monotonic Reasoning (NMR 2016)
Kern-Isberner,, Gabriele
Wassermann, Renata
Technische Universität Dortmund
133-142
Yes
No
International
16th International Workshop on Non-Monotonic Reasoning (NMR 2016)
from 22-04-2016 to 24-04-2016
Cape Town
South Africa
[en] probability ; belief revision
[en] 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.