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