Merging Knowledge Bases in Possibilistic Logic by Lexicographic Aggregation
Guilin Qi, Jianfeng Du, Weiru Liu, David A. Bell

TL;DR
This paper introduces a new possibilistic logic merging operator based on lexicographic aggregation that produces consistent knowledge bases and satisfies key minimal change principles, improving upon existing methods.
Contribution
It proposes a novel lexicographic-based merging operator for possibilistic logic that addresses inconsistency and the drowning effect, with formal properties and an algorithm for implementation.
Findings
Satisfies nine generalized merging postulates.
Ensures minimal change in knowledge base merging.
Demonstrates advantages over existing propositional merging operators.
Abstract
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 suers from the well-known "drowning effect". Therefore, we may still want to obtain a consistent possi- bilistic knowledge base as the result of merg- ing. In such a case, we argue that it is not always necessary to keep weighted informa- tion after merging. In this paper, we define a merging operator that maps a set of pos- sibilistic knowledge bases and a formula rep- resenting the integrity constraints to a…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Semantic Web and Ontologies · Bayesian Modeling and Causal Inference
