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Solving VT in VITAL: A Study in Model Construction and Knowledge Reuse

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 operationalised 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.

*Artificial Intelligence Group, Dept. of Psychology, University of Nottingham University Park, Nottingham, NG7 2RD. U.K. nrs@psychology.nottingham.ac.uk

The VITAL project is a 4.5 year research and development enterprise involving seven organisations drawn from four countries. The total effort invested is about 80 man-years. VITAL is partially funded by the ESPRIT Program of the Commision of the European Communities, as project number 5365. The partners in the VITAL project are the following: Syseca Temps Reel (F), Bull Cediag (F), Onera (F), The Open University (UK), University of Nottingham (UK), University of Helsinki (SF), and Andersen Consulting (E).

Publication(s)

International Journal of Human-Computer Studies, Special Issue on the VT Elevator Design Problem. Vol. 44 (3-4). March-April 1996.

ID: kmi-95-09

Date: 1995

Author(s): Enrico Motta, *Kieron O'Hara, *Nigel Shadbolt, Arthur Stutt and Zdenek Zdrahal

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