Tech Reports
Tech Report kmi-95-09 Abstract
Solving VT in VITAL: A Study in Model Construction and Knowledge Reuse
Techreport ID: kmi-95-09
Date: 1995
Author(s): Enrico Motta, *Kieron O'Hara, *Nigel Shadbolt, Arthur Stutt and Zdenek Zdrahal
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.
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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