Making role-limiting shells more flexible

Problem Solving Models Support Tools
Part of the Lecture Notes in Computer Science book series (LNCS, volume 723)


Although expert system shells based on role-limiting methods are very powerful in supporting expert system development by model-guided and graphical knowledge acquisition, it is a legitimate and often mentioned criticism that such shells usually are hard-wired and therefore hardly flexible if a given problem does not totally match the predefined method. In this paper we analyse the inner structure of role-limiting methods of two shells implemented within our group, and break them down into smaller mechanisms in order to enable new configurations of role-limiting methods and corresponding shells. Method configuration is supported both by offering a library of problem-solving specific mechanisms of how a subtask can be solved, and by allowing the introduction of new mechanisms and subtasks and their combination with the existing ones within the predefined framework. We demonstrate our approach both with assignment problems and with classification tasks. The gained flexibility substantially increases the applicability of role-limiting methods and — by allowing the reuse of mechanisms and user-interface — drastically reduces the costs of new method development.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bamberger, S., Gappa, U., Goos, K., Poeck, K. (1993). Supporting knowledge acquisition by semi-automatic knowledge transformations (in German), Proceedings der 2. deutschen Tagung Expertensysteme, Hamburg, Springer, 153–166.Google Scholar
  2. Benjamins, V. R., Jansweijer, W. N. H., Abu-Hanna, A. (1992). Integrating problem solving methods into KADS. Interpretation models for KADS — Proceedings of the second KADS User Meeting, Bauer, C., Karbach. W. (Eds.), GMD-Study 212.Google Scholar
  3. Burkard, R. E., Derigs, U. (1980). Assignment and Matching Problems — Solution Methods with FORTRAN Programs, Lecture notes in economics and mathematical systems 184, Springer.Google Scholar
  4. Busacker, R. G., Gowen, P. J. (1961). A Procedure for Determining a Family of Minimal Cost Network Flow Patterns. ORO Technical Report 15, Operations Research Office, John Hopkins University.Google Scholar
  5. Chandrasekaran, B. (1990). Design problem solving. A task analysis. AI Magazine.Google Scholar
  6. Chandrasekaran, B., & Johnson, T. R. (1993). Generic tasks and task structures: History, critique and new directions. Second Generation Expert Systems, David, J.-M., Krivine, J.-P., & Simmons, R. (Eds.), Springer, Berlin (to appear).Google Scholar
  7. Coulon, C. H., van Harmelen, F., Karbach, W., Voß, A. (1992). Controlling generate & test in any time, Proceedings of the GWAI-92, to appear 93 as in Lecture notes in Artificial Intelligence 671, Springer.Google Scholar
  8. Eshelman, L. (1988). MOLE: A knowledge acquisition tool for cover-and-differentiate systems. [Marcus 88a], 37–80.Google Scholar
  9. Freuder, E. C. (1989). Partial Constraint Satisfaction, Proc. IJCAI-89, 278–283.Google Scholar
  10. Gappa, U. (1991a). Graphical knowledge representations (in German). Proceedings of the German Workshop of Artificial Intelligence, Springer, Informatik Fachberichte 285, 221–230.Google Scholar
  11. Gappa, U. (1991b). A toolbox for generating graphical knowledge acquisition environments. Proceedings of the first World Congress on Expert Systems, Orlando, Liebowitz, J. (Ed.), Vol 2, Pergamon Press, 787–810.Google Scholar
  12. Gappa, U., Poeck, K. (1992). Common ground and differences of the KADS and the strong-problem-solving-shell approach. Current Developments in Knowledge Acquisition: EKAW-92, 6th European Knowledge Acquisition Workshop, Wetter, Th. et al. (Eds.), Lecture Notes in Artificial Intelligence 599, 75–94, Springer.Google Scholar
  13. Gappa, U., Puppe, F., & Schewe, S. (1993). Graphical knowledge acquisition for medical diagnostic expert systems. Artificial Intelligence in Medicine, Special Issue “Knowledge Acquisition”, 1–27, (to appear).Google Scholar
  14. Geelen, P. A. (1992). Dual viewpoint heuristics for binary constraint satisfaction problems, Proceedings of the 10th European Conference on Artificial Intelligence, 33–35.Google Scholar
  15. Goldberg, D. E. (1989). Genetic algorithms in search optimisation and machine learning, Addison Wesley.Google Scholar
  16. Hentenryk, P. (1989): Constraint Satisfaction in Logic Programming, MIT-Press.Google Scholar
  17. Johnston, M. D., Adorf, H. M. (1992). Scheduling with neural networks-the case of hubble space telescope, Computers and Operations research, Special issue on neural networks (19), 209–240.Google Scholar
  18. Karbach, W., Voß, A. (1992). An inference structure for assignment problems. Interpretation models for KADS-Proceedings of the second KADS User Meeting, Bauer, C., Karbach. W. (Eds.), GMD-Studie 212.Google Scholar
  19. Keng, N., Yun, D. (1989). A Planning/Scheduling Methodology for the Constrained Resource Problem, Proc. IJCAI-89, 998–1003.Google Scholar
  20. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimisation by Simulated Annealing, Science 200, 671–680.Google Scholar
  21. Marcus, S. (1988a). (Ed.) Automating Knowledge Acquisition for Expert Systems. Kluwer Academic, Boston.Google Scholar
  22. Marcus, S. (1988b). SALT: A knowledge acquisition tool for propose-and-revise systems. In [Marcus 88a], 81–123.Google Scholar
  23. Marques, D., Dallemagne, G., Klinker, G., McDermott, J., Tung, D. (1992). Easy programming: Empowering people to build their own applications. IEEE Expert, 7(3), 16–29.Google Scholar
  24. Matzke, R. (1993). Integration and comparison of heuristic and set covering classification (in German), Proceedings der 2. deutschen Tagung Expertensysteme, Hamburg, Springer, 56–69.Google Scholar
  25. McDermott, J. (1988). Preliminary steps toward a taxonomy of problem-solving methods. [Marcus 88a], 225–256.Google Scholar
  26. Musen, M. A., Fagan, L., Combs, D., Shortliffe, E. (1987). Use of a domain model to drive an interactive knowledge-editing tool. International Journal of Man-Machine Studies, 26, 105–121.Google Scholar
  27. Papapostolou, A. (1992). Design, implementation and integration of an expert system shell for classification with functional models (in German). Diploma thesis at the Institut für Logik, Komplexität und Deduktionssysteme, Universität Karlsruhe.Google Scholar
  28. Poeck, K., Puppe, F. (1992). COKE: Efficient solving of complex assignment problems with the propose-and-exchange method. 5th International Conference on Tools with Artificial Intelligence, Arlington, Virginia, USA, 136–143.Google Scholar
  29. Puerta, A. R., Egar, J. W., Tu, S. W., Musen, M. A. (1992). A multiple-method knowledge-acquisition shell for the automatic generation of knowledge-acquisition tools. Knowledge Acquisition (4), 171–196.Google Scholar
  30. Puppe, F. (1993). Systematic Introduction to Expert Systems. Springer.Google Scholar
  31. Puppe, F., Gappa, U. (1992). Towards knowledge acquisition by experts. Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 5th International Conference IEA/AIE-92, Paderborn, Belli, F., & Radermacher, F. J. (Eds.), 546–555, Lecture Notes in Artificial Intelligence 604, Springer.Google Scholar
  32. Puppe, F., Goos, K. (1991). Improving case based classification with expert knowledge. 15. Fachtagung für Künstliche Intelligenz, GWAI-91, Christaller, Th. (Ed.), 196–205, Informatik-Fachberichte 285, Springer.Google Scholar
  33. Radestock, G. (1993). A tool for non-graphical knowledge acquisition for expert system shells based on strong problem solving methods (in German), Diploma thesis at the Institut für Logik, Komplexität und Deduktionssysteme, Universität Karlsruhe.Google Scholar
  34. Steels, L. (1992). Reusability and configuration of applications by non-programmers. Technical Report, VUB AI Lab, Brussels.Google Scholar
  35. Walther, E., Eriksson, H., & Musen, M. A. (1992). Plug-and-play: Construction of task-specific expert-system shells using sharable context ontologies. AAAI Workshop on Knowledge Representation Aspects of Knowledge Acquisition, San Jose, California. American Association for Artificial Intelligence, 191–198.Google Scholar
  36. Wielinga, B., Schreiber, G., Breuker, J. (1992). KADS: A modelling approach to knowledge engineering. Knowledge Acquisition, 4(1), Special Issue “The KADS approach to knowledge engineering”, 5–54.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  1. 1.Lehrstuhl für Informatik VIUniversität WürzburgGerbrunnGermany
  2. 2.Institut für LogikUniversität KarlsruheKarlsruhe 1Germany

Personalised recommendations