Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation

Guilin Qi, Jianfeng Du, Weiru Liu, David Bell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
83 Downloads (Pure)


Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and deal-ing with uncertainty. They often result in a possibilistic knowledge base which is a set of weighted formulas. Although possibilistic logic is inconsistency tolerant, it suffers from the well-known "drowning effect". Therefore, we may still want to obtain a consistent possibilistic knowledge base as the result of merging. In such a case, we argue that it is not always necessary to keep weighted information after merging. In this paper, we define a merging operator that maps a set of possibilistic knowledge bases and a formula representing the integrity constraints to a classical knowledge base by using lexicographic ordering. We show that it satisfies nine postulates that generalize basic postulates for propositional merging given in [11]. These postulates capture the principle of minimal change in some sense. We then provide an algorithm for generating the resulting knowledge base of our merging operator. Finally, we discuss the compatibility of our merging operator with propositional merging and establish the advantage of our merging operator over existing semantic merging operators in the propositional case.
Original languageEnglish
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
PublisherAUAI Press
Number of pages7
ISBN (Print)9780974903965
Publication statusPublished - Jul 2010
EventThe 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) - Catalina Island, California, United States
Duration: 08 Jul 201011 Jul 2010


ConferenceThe 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Country/TerritoryUnited States

Bibliographical note

Medium of Output: Conference proceedings, AAAI press

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


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