Spherical Linguistic Petri Nets for Knowledge Representation and Reasoning under Large Group Environment

Xun Mou, Ling Xiang Mao, Hu Chen Liu, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Fuzzy Petri nets (FPNs) are a promising modeling tool for knowledge representation and reasoning of rule-based expert systems. However, there exist limitations in representing ambiguous knowledge and performing approximate inference in traditional FPNs. Additionally, knowledge parameters are usually provided by some experts in existing FPN methods. In response to these issues, a new version of FPNs, named spherical linguistic Petri nets (SLPNs), is introduced in this article for knowledge representation and reasoning in the large group context. To this end, spherical linguistic sets are applied to capture imprecise knowledge and represent the uncertainty of experts' judgements. Furthermore, a large group knowledge acquisition approach is developed to determine knowledge parameters. A bidirectional inference algorithm is developed for implementing the reasoning process and identifying the root causes of an appointed event. Finally, the efficacy and superiority of our developed SLPNs are illustrated by a realistic example regarding stampede risk level assessment in a high-speed railway station.

Original languageEnglish (US)
Pages (from-to)402-413
Number of pages12
JournalIEEE Transactions on Artificial Intelligence
Volume3
Issue number3
DOIs
StatePublished - Jun 1 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Keywords

  • Expert system
  • Fuzzy Petri net
  • High-speed railway station
  • Knowledge reasoning
  • Knowledge representation
  • Spherical linguistic set

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