A theory of diagnosis from first principles
Artificial Intelligence
Using crude probability estimates to guide diagnosis
Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
Guest Editors' Introduction: Recommender Systems
IEEE Intelligent Systems
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Representative explanations for over-constrained problems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Plausible repairs for inconsistent requirements
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A general diagnosis method for ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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Constraint-based applications such as configurators, recommenders, and scheduling systems support users in complex decision making scenarios. Typically, these systems try to identify a solution that satisfies all articulated user requirements. If the requirements are inconsistent with the underlying constraint set, users have to be actively supported in finding a way out from the no solution could be found dilemma. In this paper we introduce techniques that support the calculation of personalized diagnoses for inconsistent constraint sets. These techniques significantly improve the diagnosis prediction quality compared to approaches based on the calculation of minimal cardinality diagnoses. In order to show the applicability of our approach we present the results of an empirical study and a corresponding performance analysis.