Intelligent decision-making approach based on fuzzy-causal knowledge and reasoning

Abstract

Our intelligent decision-making approach (IDMA) is an instance of cognitive computing. It applies causality as common sense reasoning and fuzzy logic as a representation for qualitative knowledge. Our IDMA collects raw knowledge of humans through psychological models to tailor a knowledge-base (KB). The KB manages different repositories (e.g., cognitive maps (CM) and an ontology) to depict the object of study. The IDMA traces fuzzy-causal inferences to simulate causal behavior and estimate causal outcomes for decision-making. In order to test our approach, it is linked to the sequencing module of an intelligent and adaptive web-based educational system (IAWBES). It is used to provide student-centered education and enhance the students' learning by intelligent and adaptive functionalities. The results reveal users of an experimental group reached 17% of better learning than their peers of the control group. © 2012 Springer-Verlag.