A System of Serial Computation for Classified Rules Prediction in Non-Regular Ontology Trees

Abstract

Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters.

Formalizing Logic Based Rules for Skills Classification and Recommendation of Learning Materials

“…The Fig. 4 which is a non-regular ontology [47] illustrates the rule formation process using an ontology of five leafnodes N. In contrast to a regular ontology that has equal number of leafnodes across all parent class nodes, a non-regular ontology is a tree with varying number of leafnodes across its parent class node (Fig. 4).…”