A system of serial computation for classified rules prediction in non-regular ontology trees
This work addresses a specific challenge in multiagent systems for learning environments, but it appears incremental as it extends previous research on regular models to non-regular cases.
The paper tackles the problem of predicting the number of rules needed for inductive learning in non-regular ontology models, presenting a polynomial equation system that estimates this based on defined parameters.
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.