A Validated Ontology for Metareasoning in Intelligent Systems

Abstract

Metareasoning suffers from the heterogeneity problem, in which different researchers build diverse metareasoning models for intelligent systems with comparable functionality but differing contexts, ambiguous terminology, and occasionally contradicting features and descriptions. This article presents an ontology-driven knowledge representation for metareasoning in intelligent systems. The proposed ontology, called IM-Onto, provides a visual means of sharing a common understanding of the structure and relationships between terms and concepts. A rigorous research method was followed to ensure that the two main requirements of the ontology (integrity based on relevant knowledge and acceptance by researchers and practitioners) were met. The high accuracy rate indicates that most of the knowledge elements in the ontology are useful information for the integration of multiple types of metareasoning problems in intelligent systems.