A split-combination approach to merging knowledge bases in possibilistic logic

  • Authors:

  • Guilin Qi;Weiru Liu;David H. Glass;David A. Bell

  • Affiliations:

  • School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK BT7 1NN and Institute AIFB, Universität Karlsruhe, Karlsruhe, Germany D-76128;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK BT7 1NN;School of Computing and Mathematics, University of Ulster, Co.Antrim, UK BT37 0QB;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK BT7 1NN

  • Venue:
  • Annals of Mathematics and Artificial Intelligence
  • Year:
  • 2006

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Abstract

We propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: the splitting step and the combination step. The splitting step splits each knowledge base into two subbases and then in the second step, different classes of subbases are combined using different operators. Our merging approach is applied to knowledge bases which are self-consistent and results in a knowledge base which is also consistent. Two operators are proposed based on two different splitting methods. Both operators result in a possibilistic knowledge base which contains more information than that obtained by the t-conorm (such as the maximum) based merging methods. In the flat case, one of the operators provides a good alternative to syntax-based merging operators in classical logic.