Representation and Reasoning of Fuzzy Knowledge Under Variable Fuzzy Criterion Using Extended Fuzzy Petri Nets

• Published in: IEEE transactions on fuzzy systems Vol. 28; no. 12; pp. 3376 - 3390
• Main Authors: Zhou, Ruqi, Feng, Jiali, Chen, Yiqun, Chang, Huiyou, Zhou, Yuepeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Artificial intelligence; Cognition; Criteria; Education; Fuzzy knowledge representation and reasoning; Fuzzy logic; fuzzy Petri nets (FPNs); Granular materials; Heuristic algorithms; Knowledge representation; Mapping; Petri nets; Qualitative analysis; qualitative criterion transformation; qualitative mapping; Reasoning; Transformations (mathematics); Uncertainty; variable fuzzy criterion
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2019.2950883

Fuzzy knowledge representation and reasoning is one of the key research subjects of artificial intelligence with uncertainty. Currently, fuzzy Petri nets (FPNs) cannot describe fuzzy nondeterministic knowledge based on the variable fuzzy criterion clearly. To solve this problem, we present an extended FPN model based on the qualitative mapping and the qualitative criterion transformation. First, the mechanism and the principles of the variable fuzzy criterion under qualitative mapping are introduced. The important theoretical basis of qualitative mapping is detection and integration of perceptual attribute features. Under transition degree function and the qualitative criterion transformation, the qualitative mapping better reflects the cases in which the fuzzy membership criterion is variable. Second, we use the characteristics of the qualitative mapping to propose a structure of attribute fuzzy information granule. Based on the logic operation of fuzzy information granule, we redescribe the four fuzzy production rules which are commonly used, and obtain four kinds of fuzzy qualitative judgment rules. They can explicitly express the occurrence process of transition nodes of FPNs, which strengthens the uncertain knowledge representation ability of FPNs. The new method makes it easy to construct a learning mechanism based on cognition in FPNs.