Applying the REKAP methodology to situation assessment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 867)


This paper outlines a principled methodology, based on KADS, for generating runnable expert system knowledge-bases from the output of high-level knowledge acquisition tools. This methodology is based upon a synthesis of earlier work arising from the UK CONSENSUS, ESPRIT P2576 ACKNOWLEDGE and P5365 VITAL projects. REKAP integrates knowledge elicitation techniques, real-time structured analysis and a model of the the desired run-time architecture within a common framework based upon extensions to the original KADS four-layer model of expertise. The methodology has been realised as a compiler between ProtoKEW, a knowledge acquisition toolkit, structured task-analysis tools and MUSE, a real-time expert system shell. The paper focuses on a particular example of the use of the methodology, in the domain of situation assessment.


Knowledge Acquisition Knowledge Source Common Framework Requirement Model Knowledge Engineer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Anjewierden, A., Wielinga, B. and Shadbolt, N. (1992) Supporting Knowledge Acquisition: The ACKnowledge Project. In Steels, L. and Lepape, B. (Eds.) ESPRIT-92 Knowledge Engineering, CEC, Brussels.Google Scholar
  2. Bokma, A.F., Slade, A.J., Bateman, M.R., Kellaway, M. and Martin, S. (1993) The CONSENSUS Method, Final Report D17. IEATP Project 1365, British Aerospace.Google Scholar
  3. Boy, G. (1993) Knowledge Acquisition in Dynamic Systems: How Can Logicism and Situatedness Go Together? In Aussenac, N., Boy, G., Gaines, B., Linster, M., Ganascia, J-G. and Kodratoff, Y. (Eds.) Knowledge Acquisition for Knowledge-Based Systems — EKAW 93. Springer-Verlag.Google Scholar
  4. Breuker, J., Wielinga, B., Van Someren, M., De Hoog, R., Schreiber, G., De Greef, P., Bredeweg, B., Wielemaker, L., Billault, J-P., Davoodi, M. and Hayward, S. (1987) Model Driven Knowledge Acquisition: interpretation models. ESPRIT Project P1098 Deliverable D1. University of Amsterdam and STL Ltd.Google Scholar
  5. Chandrasekaran, B. (1988) Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples. The Knowledge Engineering Review, 3(3), 183–210.Google Scholar
  6. Craig, I.J. (1989) The Cassandra Architecture — Distributed Control in a Blackboard System. Ellis Horwood, UK.Google Scholar
  7. Fjellheim, R.A., Pettersen, T.B. and Christoffersen, B. (1992) REAKT Application Methodology Overview, The REAKT Consortium.Google Scholar
  8. Hatley, D.J. and Pirbhai, I.A. (1987) Strategies For Real-Time System Specification. Dorset House Publishing, New York.Google Scholar
  9. Major, N.P. (1991) CATO — An Automated Card Sort Tool. In Linster, M. and Gaines, B. (Eds.) Proceedings of EKAW 91. GMD-Studien Nr. 211, September 1992.Google Scholar
  10. Major, N.P. and Reichgelt, H. (1990) ALTO — An Automated Laddering Tool. In Wielinga B et al. (Eds.) Current Trends in Knowledge Acquisition. IOS Press, Amsterdam.Google Scholar
  11. Major, N.P. and Shadbolt, N.R. (1992) CNN — Integrating Knowledge Elicitation with a Machine Learning Technique. In Mizoguchi, R., Motoda, H., Boose, J., Gaines, B. and Quinlan, R. (Eds.) Proceedings of JKAW-92, Osaka University.Google Scholar
  12. MUSE Technical Description, Cambridge Consultants, 1987.Google Scholar
  13. Newell, A. (1982) The Knowledge Level. Artificial Intelligence, 18, 87–127.Google Scholar
  14. O'Hara, K. (1993) A Representation of KADS-I Interpretation Models Using A Decompositional Approach. In Löckenhoff, C. Fensel, D. and Studer, R. (Eds.) Proceedings of the 3rd KADS Meeting, pp 147–169. Siemens AG, Munich.Google Scholar
  15. O'Hara, K. and Shadbolt, N. (1993a) AI Models as a variety of psychological explanation. In the Proceedings of IJCAI-93, pp 188–193.Google Scholar
  16. O'Hara, K. and Shadbolt, N. (1993b) Locating Generic Tasks. Knowledge Acquisition, 5(4).Google Scholar
  17. Reichgelt, H. and Shadbolt, N. (1992) ProtoKEW: A knowledge-based system for knowledge acquisition. In Sleeman, D. and Bernsen, N. (Eds.) Research Advances in Cognitive Science, Volume 5. Artificial Intelligence, Lawrence Erlbaum.Google Scholar
  18. Shadbolt, N. (1992) Facts, fantasies and frameworks: the design of a knowledge acquisition workbench. In Schmalhofer, F., Strube, G. and Wetter, T. (Eds.) Contemporary Knowledge Engineering and Cognition. Springer Verlag, Heidelberg.Google Scholar
  19. Shadbolt, N., Motta, E. and Rouge, A. (1993) Constructing Knowledge-Based Systems. IEEE Software, November 1993, pp 34–39.Google Scholar
  20. Shaw, M. and Gaines, B. (1987) An Interactive Knowledge Elicitation Technique using Personal Construct Technology. In Kidd, A. (Ed.) Knowledge Acquisition for Expert Systems: A Practical Handbook. New York: Plenum Press.Google Scholar
  21. Terpstra, P., Van Heijst, G., Shadbolt, N. and Wielinga, B. (1993) Knowledge Acquisition Process Support Through Generalised Directive Models. In David, J-M., Krivine, J-P. and Simmons, R. (Eds.) Second Generation Expert Systems, pp 428–454. Springer-Verlag.Google Scholar
  22. Van Heijst, G., Terpstra, P., Wielinga, B. and Shadbolt, N. (1992) Using Generalised Directive Models in Knowledge Acquisition. In Wetter, T., Althoff, K-D., Boose, J., Gaines, B., Linster, M. and Schmalhofer, F. (Eds.) Current Developments in Knowledge Acquisition — EKAW 92, pp 112–132. Springer-Verlag.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  1. 1.Artificial Intelligence Group Department of PsychologyUniversity of NottinghamNottinghamEngland

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