Case Representation, Acquisition, and Retrieval
in SIROCCO

Bruce McLaren and Kevin Ashley

University of Pittsburgh
Intelligent Systems Program
3939 OHara Street
Pittsburgh, PA 15260
bmclaren+@pitt.edu, ashley+@pitt.edu



Abstract. As part of our investigation of how abstract principles are
operationalized to facilitate their application to specific fact situations, we
have begun to develop and experiment with SIROCCO (System for
Intelligent Retrieval of Operationalized Cases and COdes), a CBR retrieval
and analysis system applied to the domain of engineering ethics. SIROCCO
is intended to retrieve decided engineering ethics cases and previously
applied ethics codes to assist engineers and students in analyzing new cases.
Here we describe a limited but expressive language designed to represent a
wide range of ethics cases in SIROCCO, a world-wide web tool developed to
perform case acquisition and support a measure of consistency in
representation, and an experiment to validate the initial phase of
SIROCCOs retrieval algorithm and test its sensitivity to small variations in
case description.
References

Aleven, V. (1997). Teaching Case-Based Argumentation Through a Model and Examples.
Ph.D. Dissertation, University of Pittsburgh.
Ashley, K. D. and McLaren, B. M. (1995). Reasoning with Reasons in Case-Based
Comparisons. In the Proceedings of the First International Conference on Case-Based
Reasoning (ICCBR-95). Pp, 133-144. Lecture Notes in Artificial Intelligence 1010.
Springer Verlag. Heidelberg, Germany.
Branting, L. K. (1990) Integrating Rules and Precedents for Classification and
Explanation: Automating Legal Analysis. Ph.D. Dissertation. U. Texas at Austin AI
Lab. AI 90-146.
Branting, L. K. (1994). A Computational Model of Ratio Decidendi. Artificial Intelligence
and Law 2:1-31. Kiuwer Academic Publishers. Printed in the Netherlands.
Bruninghaus, S., and Ashley, K. D. (1997) Using Machine Learning to Assign Indices to
Legal Cases. In the Proceedings of the Second International Conference on Case-Based
Reasoning (ICCBR-97). Pp. 303-314. Lecture Notes in Artificial Intelligence 1266.
Springer Verlag. Heidelberg, Germany.
Cheng, C. H., Holsapple, C. W. and Lee, A. (1996). Citation-Based Journal Rankings for
Al Research: A Business Perspective.. AI Magazine, Vol. 17, No. 2.
Daniels, J. and E. Rissland. (1997) Finding Legally Relevant Passages in Case Opinions.
In the Proceedings of the Sixth International Conference on AI and Law (ICAIL-97). Pp.
39-46. ACM Press: New York.
Forbus, K. D., Gentuer, D. and Law, K. (1994). MAC/FAC: A Model of Similarity-based
Retrieval. Cognitive Science 19, Pp. 141-205.
Jonsen A. R. and Toulmin 5. (1988). The Abuse of Casuistry: A History of Moral
Reasoning. University of CA Press, Berkeley.
Lewis, D. D., Schapire, R. E., Callan, J. P., and Papka, R. (1996). Training Algorithms for
Linear Text Classifiers. In the Proceedings of the 19th Annual International ACM-SIGIR
Conference on Research and Development in Information Retrieval. Zurich.
McLaren, B. M. and Ashley, K. D. (1998). Exploring the Dialectic Between Abstract Rules
and Concrete Facts: Operationalizing Principles and Cases in Engineering Ethics. In the
Proceedings From the Fourth European Workshop on Case-Based Reasoning. Pp, 37-51.
Lecture Notes in Artificial Intelligence 1488. Springer Verlag. Heidelberg, Germany.
Mostow, J. (1983). Machine transformation of advice into a heuristic search procedure. In
Machine Learning, vol. 1.
NSPE (1958-1997). Opinions of the Board of Ethical Review, Vol. I - VII and NSPE Ethics
Reference Guide. Published by National Society of Professional Engineers, Alexandria,
Virginia.
Rissland, E. L., Skalak, D. B., and Friedman, M. T. (1996). BankXX: Supporting Legal
Arguments through Heuristic Retrieval. AI and Law 4:1-71. Kluwer, Dordrecht.
Thagard, P., Holyoak, K., Nelson, G., and Gochfeld, (1990). Analog Retrieval by
Constraint Satisfaction, Artificial Intelligence 46, Pp. 259-310.
van Rijsbergen, C. J. (1979). Information Retrieval. Butterworths, London, second edition
