Fuzzy Cognitive Map and Bayesian Belief Network for Causal Knowledge Engineering: A Comparative Study

Abstract

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

“…Causal-based and model-driven systems such as FCMs and Bayesian belief networks (BBNs) are based on visual graphs consisting of nodes (variables) and directional links between nodes that represent cause-andeffect relationships between the variables. e research findings show that in comparison to BBNs, FCMs are more suitable for use as a front-end modeling tool to elicit expert knowledge, since the causal model is simpler, more intuitive, and user-friendly, making easier their composition and decomposition [42,43].…”

Fuzzy Cognitive Maps-Based Switched-Mode Power Supply Design Assistant System

“…Building causal knowledge system is important for assessing human performances, in that the knowledge system determines what kinds of knowledge and skills should be evaluated from human. Generally, Bayesian belief network (BBN) and Fuzzy Cognitive Map (FCM) were used to build the causal knowledge system [8]. For instance, Mislevy and Gitomer [9] constructed BBN in aircraft hydraulics system, to help learners conduct troubleshooting tasks.…”

“…In this study, we use FCM to construct the knowledge system in that we integrate different perspectives of stakeholders. Because the structure of the domain variables in FCM is more intuitive and user friendly than the tabular interface provided by the conditional probability tables of BBN, it will facilitate the process of domain modeling [8].…”