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

Mou, Xun; Mao, Ling-Xiang; Liu, Hu‐Chen; Zhou, MengChu

doi:10.1109/tai.2022.3140282

Cited by 24 publications

(1 citation statement)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Ref. [15], the authors suggested (a) a new type of Petri‐net called spherical linguistic Petri‐net using spherical language sets to facilitate knowledge representation and reasoning, (b) the acquisition of knowledge from a large group of experts to determine knowledge parameters, and (c) an inference algorithm to perform reasoning. Moreover, they show that the presented model is efficient against the uncertainty of experts' opinions and the complexity of rule‐based systems.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy coloured petri nets‐based method to analyse and verify the functionality of software

Chavoshi,

Babamir

2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Some types of software systems, like event‐based and non‐deterministic ones, are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules. However, when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables, they constitute a complex form that is difficult to understand and infer. A solution is to visualise the system specification with the capability of automatic rule inference. In this study, by representing a high‐level system specification, the authors visualise rule representation and firing using fuzzy coloured Petri‐nets. Already, several fuzzy Petri‐nets‐based methods have been presented, but they either do not support a large number of rules and variables or do not consider significant cases like (a) the weight of the premise's propositions in the occurrence of the rule conclusion, (b) the weight of conclusion's proposition, (c) threshold values for premise and conclusion's propositions of the rule, and (d) the certainty factor (CF) for the rule or the conclusion's proposition. By considering cases (a)–(d), a wider variety of fuzzy rules are supported. The authors applied their model to the analysis of attacks against a part of a real secure water treatment system. In another real experiment, the authors applied the model to the two scenarios from their previous work and analysed the results.

show abstract

Section: Related Workmentioning

confidence: 99%