Consistency-based approaches to merging knowledge bases: preliminary report

We present a framework for investigating merging operators for belief sets. This framework is a generalisation of our earlier work concerning consistency-based belief revision and contraction. Two distinct merging operators are identified: in the first approach, belief sources are consistently combined so that the result of merging K1 and K2 is a maximal consistent (if possible) set of formulas comprising the joint knowledge of K1 and K2. This approach then accords to one’s intuitions as to what a “merge” operator should do. The second approach is more akin to a generalised belief revision operator: Two knowledge sources are “projected” onto a third (in the simplest case the trivially true knowledge base). In both cases, we consider the incorporation of entailment-based and consistency-based integrity constraints. Properties of these operators are investigated, primarily by comparing their properties with postulates that have been identified previously in the literature. ...

• Type: Conference
• Year: 2004
• Where: NMR
• Authors: James P. Delgrande, Torsten Schaub
• Keywords: Artificial Intelligence, Belief Revision, Consistency-based Belief Revision, Merging Operators, NMR 2004