Abstract
This article introduces a technique for improving the efficiency of diagnosis through approximate compilation. We extend the approach of compiling a diagnostic model, as is done by, for example, an ATMS, to compiling an approximate model. Approximate compilation overcomes the problem of space required for the compilation being worst-case exponential in particular model parameters, such as the path-width of a model represented as a Constraint Satisfaction Problem. To address this problem, we compile the subset of most “preferred” (or most likely) diagnoses. For appropriate compilations, we show that significant reductions in space (and hence on-line inference speed) can be achieved, while retaining the ability to solve the majority of most preferred diagnostic queries. We experimentally demonstrate that such results can be obtained in real-world problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
de Kleer, J.: An Assumption-based TMS. AI Journal 28, 127–162 (1986)
Darwiche, A.: A compiler for deterministic, decomposable negation normal form. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI), Menlo Park, California, pp. 627–634. AAAI Press, Menlo Park (2002)
Console, L., Portinale, L., Dupre, D.T.: Using Compiled Knowledge to Guide and Focus Abductive Diagnosis. IEEE Trans. on Knowledge and Data Engineering 8(5), 690–706 (1996)
Darwiche, A.: Model-based diagnosis using structured system descriptions. Journal of Artificial Intelligence Research 8, 165–222 (1998)
Bodlander, H.: Treewidth: Algorithmic techniques and results. In: Privara, I., Ružička, P. (eds.) MFCS 1997. LNCS, vol. 1295, pp. 29–36. Springer, Heidelberg (1997)
de Kleer, J.: Focusing on Probable Diagnoses. In: Proc. AAAI, pp. 842–848 (1991)
Sachenbacher, M., Williams, B.: Diagnosis as semiring-based constraint optimization. In: Proceedings of ECAI 2004, Valencia, Spain (2004)
Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint logic programming: syntax and semantics. ACM TOPLAS 23 (2002)
Bryant, R.E., Meinel, C.: Ordered binary decision diagrams. In: Hassoun, S., Sasao, T. (eds.) Logic Synthesis and Verification. Kluwer Academic Publishers, Dordrecht (2001)
Pargamin, B.: Extending Cluster Tree Compilation with non-Boolean Variables in Product Configuration. In: IJCAI (2003)
Selman, B., Kautz, H.: Knowledge compilation and theory approximation. Journal of the ACM 43, 193–224 (1996)
del Val, A.: An analysis of approximate knowledge compilation. In: Proc. IJCAI, pp. 830–836 (1995)
Darwiche, A., Marquis, P.: Compilation of weighted propositional knowledge bases. In: Proceedings of the Workshop on Nonmonotonic Reasoning, Toulouse, France (2002)
Spohn, W.: Ordinal conditional functions: A dynamic theory of epistemic states. In: Harper, W.L., Skyrms, B. (eds.) Causation in Decision, Belief Change, and Statistics, pp. 105–134. Reidel, Dordrecht (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Provan, G. (2005). Approximate Model-Based Diagnosis Using Preference-Based Compilation. In: Zucker, JD., Saitta, L. (eds) Abstraction, Reformulation and Approximation. SARA 2005. Lecture Notes in Computer Science(), vol 3607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527862_13
Download citation
DOI: https://doi.org/10.1007/11527862_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27872-6
Online ISBN: 978-3-540-31882-8
eBook Packages: Computer ScienceComputer Science (R0)