Artificial Intelligence
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
Semiring-based constraint logic programming: syntax and semantics
ACM Transactions on Programming Languages and Systems (TOPLAS)
Ordered binary decision diagrams
Logic Synthesis and Verification
Using Compiled Knowledge to Guide and Focus Abductive Diagnosis
IEEE Transactions on Knowledge and Data Engineering
Treewidth: Algorithmoc Techniques and Results
MFCS '97 Proceedings of the 22nd International Symposium on Mathematical Foundations of Computer Science
A compiler for deterministic, decomposable negation normal form
Eighteenth national conference on Artificial intelligence
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
An analysis of approximate knowledge compilation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Focusing on probable diagnoses
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
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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.