An evaluation of the role of fuzzy cognitive maps and Bayesian belief networks in the development of causal knowledge systems

Yit Yin Wee, Wooi Ping Cheah, Shing Chiang Tan, Kuokkwee Wee

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

Fuzzy cognitive maps (FCM) and Bayesian belief networks (BBN) are two of the most frequently used causal knowledge frameworks for modelling, representing and reasoning about causal knowledge. In this paper, an evaluation of their different roles in the engineering process of developing causal knowledge systems is conducted, based on their inherent features. The evaluation criteria adopted in this research are understandability, usability, modularity, scalability, expressiveness, inferential capability, rigour, formality and preciseness. All of these are commonly used to evaluate the strengths and weaknesses of traditional knowledge representation frameworks. These criteria are used to reveal the fundamental characteristics of FCM and BBN. The findings of this study show that FCM is more appropriate for use in modelling causal knowledge, whereas BBN is more superior in model representation and inference. This study deepens the understanding of the role of FCM and BBN in the development of causal knowledge systems.

Original languageEnglish
Pages (from-to)1905-1920
Number of pages16
JournalJournal of Intelligent and Fuzzy Systems
Volume37
Issue number2
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Bayesian belief networks
  • causal knowledge systems
  • evaluation
  • Fuzzy cognitive maps
  • knowledge engineering

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering (all)
  • Artificial Intelligence

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