A system of serial computation for classified rules prediction in non-regular ontology trees

TL;DR

This paper introduces a polynomial equation-based system to predict the number of rules needed in non-regular ontology models, extending previous regular ontology models for multiagent learning environments.

Contribution

It presents a novel mathematical approach for estimating rules in non-regular ontologies, expanding the applicability of rule prediction in complex models.

Findings

The polynomial system accurately predicts rule counts in non-regular ontologies.

The approach generalizes previous regular ontology models.

It enhances decision-making in multiagent systems.

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.