Multicontext systems provide an effective representation and reasoning framework for integrating heterogeneous knowledge obtained from different sources and have been applied in different fields. Because many application fields in real life have to deal with uncertain and fuzzy knowledge, this article aims to combine the multicontext system and fuzzy logic theory effectively and systematically to deal with the representation and reasoning of uncertainty in heterogeneous contexts. The current research in this area is still relatively limited, especially in terms of systematic integration. Specifically, this article proposes a class of heterogeneous nonmonotonic fuzzy multicontext systems based on nonmonotonic multicontext systems, in which an abstract logic is proposed to capture different types of logic and is used as a theoretical basis for fuzzy multicontext knowledge representation and setting up bridging rules to integrate heterogeneous knowledge. Fuzzy equilibria are used to describe the semantics of fuzzy multicontext systems. The syntactic and semantic framework of heterogeneous nonmonotonic fuzzy multicontext systems is then systematically established. Finally, we show that the proposed fuzzy multicontext system not only extends the nonmonotonic multicontext system to fuzzy settings, but also could expand the probabilistic multicontext system and the possibilistic multicontext system in the similar way.
|Number of pages||15|
|Journal||IEEE Transactions on Fuzzy Systems|
|Early online date||8 Jul 2022|
|Publication status||Published (in print/issue) - 31 Mar 2023|
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61976065 and Grant U1836205, and in part by Guizhou Science Support Project under Grant 2022-259.
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- Abstract logics
- Probabilistic logic
- fuzzy equilibria
- knowledge integration
- multi-context systems
- multicontext systems (MCSs)