Artificial Intelligence
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
Consistency restoriation and explanations in dynamic CSPs----application to configuration
Artificial Intelligence
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
A survey on knowledge compilation
AI Communications
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
Integrating CSP decomposition techniques and BDDs for compiling configuration problems
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Constraint optimization and abstraction for embedded intelligent systems
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
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The use of embedded technology has become widespread. Many complex engineered systems comprise embedded features to perform self-diagnosis or self-reconfiguration. These features require fast response times in order to be useful in domains where embedded systems are typically deployed. Researchers often advocate the use of compilation-based approaches to store the set of environments (resp. solutions) to a diagnosis (resp. reconfiguration) problem, in some compact representation. However, the size of a compiled representation may be exponential in the treewidth of the problem. In this paper we propose a novel method for compiling the most preferred environments in order to reduce the large space requirements of our compiled representation. We show that approximate compilation is an effective means of generating the highest-valued environments, while obtaining a representation whose size can be tailored to any embedded application. The method also provides a graceful way to tradeoff space requirements with the completeness of our coverage of the environment space.