Knowledge Engineering for CBR systems
from a Cognitive Science Perspective

G. Strube, A. Enzinger, D. Janetzko & M. Knauff
Center/or Cognitive Science, University of Freiburg
IIG. Friedrichstr. 50, D-79098 Freiburg, Germany
{strube | enzinger | dietmar | knauff}@cognition.iig.uni-freiburg.de



Abstract

Although CBR has been advertised as a technique to elude knowledge engineering (RE), 
we argue that knowledge-level modeling in KE is of eminent importance 
to the success of CBR systems, both for practical and theoretical reasons.
Cases are knowledge structures linked to some underlying database (although
not necessarily in a one-to-one fashion), and in order to define case structures
and their relations to the database, domain knowledge is needed. In this paper,
we focus on KE for CBR in the domain of architectural design, first looking at
general analyses of work processes and information use, then discussing micro-analyses 
of task structure in order to define case size, finally proceeding to
knowledge-level evaluation of the domain knowledge acquired and modeled so
far.


References
Chandrasekaran, B. (1986). Generic tasks in knowledge-based reasoning: High-level building
blocks for expert system design. IEEE Expert, 1, 23-30.
Clancey, W. J. (1985). Heuristic dassification. A rtificial Intelligence, 27,289-350.
Burkhardt, J.-M., & Dtienne, F. (1995) An empirical study of software reuse by experts in
object-oriented design. Proceedings of Interact 95. Lillehammer (Norway), 27-29 June.
Drury, C. G., Paramore, B., van Cott, H. P., Grey, S. M., & Corlett, E. N. (1987) Task
analysis. In G. Salvendy (Ed.), Handbook of Human Factors (370-401).New York: Wiley.
Enzinger, A. (forthcoming). Experts  working input. Phil. Diss., University of Freiburg.
Janetzko, D., Brner, K., Jschke, O. & Strube, G. (1994). Task-oriented Knowledge Acquisition 
and Reasoning for Design Support Systems. Proceedings of the First European Conference on
Cognitive Science in Industry, 28th - 30th September, 1994, Luxembourg, 153-184.
Kolodner, J. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmnn.
Newell, A. (1982). The knowledge level. Artificial Intelligence, 18, 87-12.
Riesbeck, C. K., & Schank, R. C. (1989). Inside case-based reasoning. Hillsdale, NJ: Erlbaum.
Schreiber, G., Wielinga, B., & Breuker, J. (1993). KADS. A principled approach to knowledge-based 
systems development. London: Academic Press.
Steels, L. (1990, Summer). The components of expertise. AI Magazine. (also VUB AI Lab
Memo 88-16).
Strube, G., Janetzko, D. & Knauff, M. (in press). Cooperative construction of expert
knowledge. In P. B. Baltes & U. M. Staudinger (Eds.), Interactive minds. Cambridge:
Cambridge University Press.
Van de Velde, W. (1993). Issues in knowledge-level modelling. In J. M. David, J. P. Krivine,
& R. Simmons (Eds.), Second Generation Expert Systems (pp. 211-231). Berlin: Springer.
VanLehn, K. (1989). Problem solving and cognitive skill acquisition. In M. I. Posner (Ed.),
Foundations of cognitive science (pp. 527-579). Cambridge, MA: MIT Press (Bradford)
Visser, W. (1987). Strategies in Programming programmable controllers: field study on a
professional programmer. In G. Olson, S. Sheppard, & E. Soloway (Eds.), Empirical Studies
of Programmers. Second Workshop. Norwood, NJ: Ablex.
Wielinga, B. J., & Breuker, J. A. (1986). Training of knowledge engineers using a structured
methodology. In T. Bernold (Ed.), Expert Systems and Knowledge Engineering (pp. 133-139).
Amsterdam: Elsevier (North-Holland).
Wielinga, B. J., Schreiber, A. T., Breuker, J. A. (1992). KADS: A modelling approach to
knowledge engineering. Knowledge Acquisition, 4 (Special Issue The KADS approach to
knowledge engineering).
Wielinga, B., Van de Velde, W., Schreiber, G., & Akkermans, H. (1993). Towards a
unification of knowledge modelling approaches. In J. M. David, J. P. Krivine, & R. Simmons
(Eds.), Second Generation Expert Systems (pp. 299-335). Berlin: Springer.
