Dynamic fuzzy knowledge networks: a kind of hybrid model for the individual teaching in ICAI

An intelligent computer aided instruction (ICAI) system suitable for individual teaching should at least provide with the following functions: 1) judging student´s understanding of the material being taught; 2) preparing different materials and choosing different teaching methods; and 3) correcting students´ answers. So an efficient software support for individual teaching activities in ICAI requires the ability to represent inexact knowledge, to carry out approximate reasoning, and to dynamically adjust the rule-base depending on specific situations. We proposed a new model: the dynamic fuzzy knowledge model which is an integration of rule-based, neural network, and fuzzy reasoning techniques. It makes use of the symbolic logic inference at the conceptual level, and neural computing as nonlinear mapping with self-organisation capability at the parameter/data level. The fuzzy concept works as a “bridge” between two different computing paradigms. The proposed model can be useful not only in the field of ICAI, but also in other applications of intelligent information processing systems