Using Case-Based Reasoning to Focus
Model-Based Diagnostic Problem Solving

Luigi Portinale, Pietro Torasso, Carlo Ortalda, Antonio Giardino

Dipartimento di Informatica - Universita di Torino
G.so Svizzera 185 - 10149 Torino (Italy)


Abstract. The aim of this paper is to present an approach to the integration 
of Case-Based Reasoning with Model-Based Reasoning in diagnostic 
problem solving. Such an integration is exploited by defining
adaptation criteria on solutions retrieved by a case-based reasoner, in
order to focus the model-based reasoner in the search for the solution
of the current case and avoiding, as much as possible, the computation
of the solution from scratch. Such adaptation criteria strictly rely on a
formal theory of diagnosis that allows us to define different adaptation
levels, relative to the trade-off between accuracy of the solution and
computational effort. A simple example in the domain of car engine
faults is presented and some important aspects are finally pointed out
on the basis of our preliminary experiments.
References

1.	D.S. Aghassi. Evaluating case-based reasoning for heart failure diagnosis. Technical 
report, Dept. of EECS, MIT, Cambridge, MA, 1990.
2.	K.D. Ashley and E.L. Rissland. Compar and contrast, a test of expertise. In Proc.
6th AAAI, pages 273278, Seattle, 1987.
3.	P.P. Bonissone and S. Dutta. Integrating case-based and rule-based reasoning: the
possibilistic connection. In Proc. 6th Conf. on Uncertainty in Artificial Intelligence, 
Cambridge, MA, 1990.
4.	L. Console, L. Portinale, and D. Theseider Dupr. Focusing abductive diagnosis.
In Proc. 11th Int. Conf. on Expert Systems and Their Applications (Conf. on 2nd
Generation Expert Systems), pages 231242, Avignon, 1991. Also in AI Commu-
nications 4(2/3):88-97, 1991.
5.	L. Console, L. Portinale, D. Theseider Dupr, and P. Torasso. Combining heuristic
and causal reasoning in diagnostic problem solving. In J.M. David, J.P. Krivine,
and R. Simmons, editors, Second Generation Expert Systems, pages 46,68. Springer
Verlag, 1993.
6.	L. Console and P. Torasso. A spectrum of logical definitions of model-based diagnosis. 
Computational Intelligence, 7(3): 133141, 1991.
7.	J.M. David, J.P. Krivine, and R. Simmons (eds.). Second Generation Expert Systems. 
Springer Verlag, 1993.
8.	J. de Kleer. Focusing on probable diagnoses. In Proc. AAAI 91, pages 842848,
Anaheim, CA, 1991. Also in [11].
9.	A. Goel. Integration of case-based reasoning and model-based reasoning for adaptive 
design problem solving. Technical report, (PhD Diss.) Dept. of Comp. and
Inf. Science, Ohio Univ., 1989.
10.	K.J. Hammond. Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, 1989.
11.	W. Hamscher, L. Console, and J. de Kleer. Readings in Model-Based Diagnosis.
Morgan Kaufmann, 1992.
12.	Y. Jang. HYDI: a hybrid system with feedback for diagnosing multiple disorders.
Technical report, MIT/LCS/TR-576, 1993.
13.	E.K. Jones. Model-based case adaptation. In Proc. AAAI 92, pages 673678, San
Jose, 1992.
14.	J. Kolodner and R. Kolodner. Using experience in clinical problem solving: Introduction 
and framework. IEEE Trans. on Systems, Man and Cybernetics,
17(3):420431, 1987.
15.	J.L. Kolodner. Retrieval and Organization Strategies in Conceptual Memory: a
Computer Model. Lawrence Erlbaum, 1984.
16.	P. Koton. Using experience in learning and problem solving. Technical report,
MIT/LCS/TR-441, 1989.
17.	D. Macchion and D.P. Vo. A hybrid KBS for technical diagnosis learning and
assistance. In Proc. EWCBR 93, pages 307312, Kaiserslautern, 1993.
18.	G. Pews and S. Wess. Combining case-based and model-based approaches for diagnostic 
applications in technical domains. In Proc. EWCBR 93, pages 325328,
Kaiserslautern, 1993.
19.	E.L. Rissland and D.B. Skalak. Combining case-based and rule-based reasoning:
a heuristic approach. In Proc. 11th IJ CA I, pages 524530, Detroit, 1989.
20.	P. Torasso and L. Console. Diagnostic Problem Solving: Combining Heuristic, Approximate 
and Causal Reasoning. Van Nostrand Reinhold, 1989.
21.	P. Torasso, L. Portinale, L. Console, and M. Casassa Mont. Approximate reasoning 
in a system combining prototypical knowledge with case-based reasoning. In
L.A. Zadeh and J. Kacprzyk, editors, Fuzzy Logic for the Management of Uncertainty. 
John Wiley & Sons, 1992.
