Computational Tool to Support the Process of Teaching and Learning in the Discipline of Artificial Intelligence

Abstract

The University of Computer Science is an educational-production centre that develops computer applications and services in different areas of knowledge. Because of the added value that the developed applications get when Artificial Intelligence techniques are used for decision making, the academic formation of the students in the different disciplines of the profession with emphasis on Artificial Intelligence is of high interest. This work describes a computational tool to support teaching that uses the conceptual algorithms of logical combinatorial recognition to determine the architecture and the set of initial weights of an artificial neural network, which contributes to the temporal efficiency of the learning process of the network and the efficiency of the classification. Experiments using Friedman’s test and cross validation method demonstrate the applicability of this hybrid model in a Multilayer Perceptron.