2016/04/07 by Gavin Rens, Thomas Meyer, Rens, Gavin +3
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #cs.AI
paper · pdf · doi:10.48550/arxiv.1604.02133
Presented at the Sixteenth International Workshop on Non-Monotonic Reasoning, 22-24 April 2016, Cape Town, South Africa. 9.25 pages
arxiv created 2016/04/07 · openalex publication_date 2016/04/07 · arxiv updated 2016/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.