A theory of diagnosis from first principles
Artificial Intelligence
A correction to the algorithm in Reiter's theory of diagnosis
Artificial Intelligence
Modeling digital circuits for troubleshooting
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Hierarchical model-based diagnosis
Readings in model-based diagnosis
What's in SD?: Towards a theory of modeling for diagnosis
Readings in model-based diagnosis
Model-based diagnosis of hardware designs
Artificial Intelligence
Configuring Large Systems Using Generative Constraint Satisfaction
IEEE Intelligent Systems
A classification and constraint-based framework for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Towards a generic model of configuraton tasks
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Diagnosing and solving over-determined constraint satisfaction problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
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Debugging, validation, and maintenance of configurator knowledge bases are important tasks for the successful deployment of product configuration systems. Consistency-based diagnosis has shown to be a promising approach for detecting faulty parts in the knowledge bases and explaining unexpected behavior of the configurator, whereby (partial) configurations are used as test cases. In this paper we show how hierarchical diagnosis can be employed to cope with the complexity of debugging large configurator knowledge bases. A framework for hierarchical diagnosis on different levels of abstraction is presented as well as an algorithm for the calculation of diagnoses on those levels. The presented approach aims at the reuse of existing special purpose configuration systems. We show that the exploitation of hierarchies in such problem domains leads to a significant efficiency enhancement thus broadening the applicability of consistency-based diagnosis.