Belief change based on global minimisation
IJCAI'07: Proceedings of the 20th international joint conference on Artifical intelligence, page 2462--2467. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2007)Abstract
A general framework for minimisation-based belief change is presented. A problem instance is made up of an undirected graph, where a formula is associated with each vertex. For example, vertices may represent spatial locations, points in time, or some other notion of locality. Information is shared between vertices via a process of minimisation over the graph. We give equivalent semantic and syntactic characterisations of this minimisation. We also show that this approach is general enough to capture existing minimisation-based approaches to belief merging, belief revision, and (temporal) extrapolation operators. While we focus on a set-theoretic notion of minimisation, we also consider other approaches, such as cardinality-based and prioritybased minimisation.
