Method for knowledge acquisition based on image processing for decision-making systems

Abstract

This work proposes a method of acquiring and updating knowledge based on image processing with application in modern decision-making systems. At the basis of the research carried out is the behavioral model of human beings, which serves as a source of inspiration for the development of the algorithm for acquiring, interpreting, classifying and presenting knowledge. To this end, an empirical analysis of methods and models of knowledge presentation in artificial intelligence systems was carried out. The extraction of new knowledge provides for the acquisition and processing of images based on the stratified Morse Theory, which allows the identification and classification of objects, their placement in depth, in order to update the knowledge specified as real or hidden. The knowledge model is presented in object format compatible with object-oriented programming languages.