Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework
This work addresses the challenge of integrating uncertain knowledge bases for applications in AI and decision-making, but it appears incremental as it builds on existing possibilistic logic methods.
The paper tackles the problem of merging uncertain information from multiple sources in possibilistic logic by proposing syntactic combination rules and an extended logic framework, resulting in a sound and complete proof system.
This paper addresses the problem of merging uncertain information in the framework of possibilistic logic. It presents several syntactic combination rules to merge possibilistic knowledge bases, provided by different sources, into a new possibilistic knowledge base. These combination rules are first described at the meta-level outside the language of possibilistic logic. Next, an extension of possibilistic logic, where the combination rules are inside the language, is proposed. A proof system in a sequent form, which is sound and complete with respect to the possibilistic logic semantics, is given.