A Principled Analysis of Merging Operations in Possibilistic Logic
This work addresses the fusion of uncertain information for AI and logic systems, but it appears incremental as it extends existing postulates to a prioritized setting.
The paper tackles the problem of merging uncertain information in possibilistic logic by analyzing different classes of merging operators, such as conjunctive and adaptive ones, and proposes an analysis based on postulates extended to handle priorities.
Possibilistic logic offers a qualitative framework for representing pieces of information associated with levels of uncertainty of priority. The fusion of multiple sources information is discussed in this setting. Different classes of merging operators are considered including conjunctive, disjunctive, reinforcement, adaptive and averaging operators. Then we propose to analyse these classes in terms of postulates. This is done by first extending the postulate for merging classical bases to the case where priorites are avaialbe.