Abstract

In this paper we discuss a solution to the Sisyphus II elevator design problem developed using the VITAL approach to structured knowledge-based system development. In particular we illustrate in detail the process by which an initial model of Propose & Revise problem solving was constructed using a generative grammar of model fragments and then refined and operationalized in the VITAL operational conceptual modelling language (OCML). In the paper we also discuss in detail the properties of a particular Propose & Revise architecture, called “Complete-Model-then-Revise”, and we show that it compares favourably in terms of competence with alternative Propose & Revise models. Moreover, using as an example the VT domain ontology provided as part of the Sisyphus II task, we critically examine the issues affecting the development of reusable ontologies. Finally, we discuss the performance of our problem solver and we show how we can use machine learning techniques to uncover additional strategic knowledge not present in the VT domain.