Model Libraries: Knowledge Representation and Reasoning

Abstract

We propose a knowledge representation and operators for large, diverse model libraries. The knowledge representation distinguishes between model types that are classes of models defined by a collection of assumptions and model templates and instances that instantiate model types through decomposition and specialization of components. Underlying the knowledge representation are inheritance to represent the level of generalization and multiple levels of instantiation to represent the level of details among models. For inheritance, we define partial order relations for models and their components including domains, classes, units, assumptions, metrics, and constraints. For instantiation, we define constraints on transformations between model types, templates, and instances. The inexact search operators support content-based retrieval especially when it is difficult for a user to exactly specify the characteristics of models of interest. The operators use similarity functions to measure the closeness of a match between a search atom and the value of a model attribute. The explanation operators are designed to enhance users' understanding about the symbols, assumptions, components, and relationships among models. A prototype system has been implemented in Prolog to demonstrate the essential features of the model library system. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.