Qualitative Knowledge to Support Reasoning About
Cases

Robert J. Aarts and Juho Rousu
VTT Biotechnology and Food Research
P.O. Box 1500, FIN02044 VTT, Finland
{Robert.Aarts, Juho.Rousu}@vtt.fi



Abstract. Our recipe planner for bioprocesses, Sophist, uses a semi-qualitative
model to reason about cases. The model represents qualitative knowledge about
the possible effects of differences between cases and about the possible causes
of observed problems. Hence, the model is a crucial resource of adaptation
knowledge. The model representation has been developed specifically to
support CBR tasks. The essential notion in this representation is that of an
influence. Representation of domain knowledge in an influence graph and a
mapping of case-features onto nodes of such a graph, enable a variety of
interesting reasoning tasks. Examples of such task illustrate how qualitative
reasoning and case-based reasoning support each other in complex planning
tasks.
References
Aamodt, A. (1994). Explanation-driven case-based reasoning, In S. Wess, K. Althoff, M.
Richter (Eds.): Topics in Case-based reasoning. Springer Verlag, pp. 274-288.

Aarts, R.J. & Rousu, J. (1996). Towards CBR for bioprocess planning. In Smith I., Faltings, B.,
(Eds.): Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence,
1186: 16-27.

Ashley, K.D. & Aleven, V. (1996). How different is different? Arguing About the Significance
of Similarities and Differences. In Smith I., Faltings, B., (Eds.): Proceedings of EWCBR-
96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 1-15.

Bhatta, S., Goel, A. & Prabhakar, 5. (1994). Innovation in Analogical Design: A Model-Based
Approach. Proc. of the Third International Conference on Al in Design, Aug. 1994,
Lausanne, Switzerland.

DeJong, G. F. (1994). Learning to plan in continuous domains. Artificial Intelligence 65: 71
141.

Falkenhainer, B., Forbus, K.D. & Gentner, D. (1989). The Structure-Mapping Engine:
Algorithm and Examples. Artificial Intelligence 41:1-63.

Forbus. K.D. (1984). Qualitative Process Theory. Artificial Intelligence 24: 85168.

Hammond, K. (1990). Explaining and Repairing Plans That Fail. Artificial Intelligence,
45: 173228.

Hanney, K. & Keane, M.T. (1996). Learning Adaptation Rules from a Case-Base. In Smith I.,
Faltings, B., (Eds). Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial
Intelligence, 1186: 179-192.

Hastings, J.D., Branting, L.K. & Lockwood, J.A. (1995), Case Adaptation Using an Incomplete
Causal Model. In: Veloso, M. & Aamodt, A. (Eds.): Proceedings ICCBR-95, Sesimbra,
Lecture Notes in Artificial Intelligence, 1010: 18 1192.

Leake, D.B., Kinley, A. & Wilson, D. Learning to Improve Case Adaptation by Introspective
Reasoning and CBR. In: Veloso, M. & Aamodt, A. (Eds.): Proceedings ICCBR-95,
Sesimbra, Lecture Notes in Artificial Intelligence, 1010: 229240.

Kamp, G. (1996). Using Description Logics for Knowledge Intensive Case-Based Reasoning.
In Smith I., Faltings, B., (Eds). Proceedings of EWCBR-96, Lausanne, Lecture Notes in
Artificial Intelligence, 1186: 204-218

Koton, P. (1989). Using experience in learning and problem solving. Massachusetts Institute of
Technology, Laboratory of Computer Science (Ph.D. diss., October 1988), MIT/LCS/TR-
441.

Nayak P. & Joskowicz, L. (1996). Efficient compositional modeling for generating causal
explanations. Artificial Intelligence 83: 193-227.

Richter, M. (1995). The similarity Issue in CBR : The knowledge contained in similarity
measures, Invited talk at ICCBR -95, Sesimbra.

Say, A.C.C. & Kuru, 5. (1996). Qualitative system identification: deriving structure from
behavior. Artificial Intelligence 83: 75-141.

Schank, R.C. & Leake, D.B. (1989). Creativity and Learning in a Case-Based Explainer.
Artificial Intelligence 40: 353-385.

Sycara, K., Guttal, R., Koning, J., Narasimhan, S. & Navinchandra, D. (1992) CADET: a Case-based 
Synthesis Tool for Engineering Design, Intl. J. Expert Systems, 4:2.
