Considering Artificial Intelligence Techniques to Perform Adaptable Knowledge Structures

Abstract

Concept Maps are considered a learning technique as a way to increase meaningful learning in the sciences, they are used for the knowledge management and to exchange knowledge Concept Maps provide a framework for making the internal knowledge explicit in a visual form that can easily be examined and shared In this paper the authors consider some Artificial Intelligence techniques to elaborate Adaptable Concept Maps, where is possible to navigate in a guide mode and not in a free way as it is conceived in the normal Concept Maps The approaches use Case-Based Systems, Bayesian Nets, Petri Nets combined with Neural Nets, and Fuzzy Cognitive Maps An adaptable behavior is supported using knowledge representation forms and inference mechanisms for the decision making The proposed models have been implemented in computational systems for different applications, all of them used by users that are not expert in the Computer Science field to generate them own adaptive systems