Theory of linear and integer programming
Theory of linear and integer programming
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Effective and Efficient Knowledge Base Refinement
Machine Learning
Automating Knowledge Acquisition for Expert Systems
Automating Knowledge Acquisition for Expert Systems
A concurrent processing framework for the set partitioning problem
Computers and Operations Research
Case-Based Reasoning Technology, From Foundations to Applications
Case-Based Reasoning Technology, From Foundations to Applications
EUROVAV '99 Collected papers from the 5th European Symposium on Validation and Verification of Knowledge Based Systems - Theory, Tools and Practice
The Adaption Knowledge Bottleneck: How to Ease it by Learning from Cases
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Higher Order Refinement Heuristics for Rule Validation
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Fallbezogene Revision und Validierung von regelbasiertem Expertenwissen für die Altlastenbeurteilung
Informatik für den Umweltschutz, 5. Symposium
Introduction to Operations Research and Revised CD-ROM 8
Introduction to Operations Research and Revised CD-ROM 8
Knowledge Maintenance of Case-Based Reasoning Systems: The Siam Methodology (Dissertations in Artificial Intelligence)
Optimal refinement of rule bases
AI Communications
Case-based adaptation for automotive engine electronic control unit calibration
Expert Systems with Applications: An International Journal
Semantic spam filtering from personalized ontologies
Journal of Web Engineering
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Rule-based systems have been successfully applied for adaptation. But the rule-based adaptation knowledge for engineering design has no static characteristic. Therefore the adaptation problem emerges also as a validation and refinement problem to be solved by global CBR approaches in an optimal way. The optimal refinement of engineering rule bases for adaptation improves the performance of expert systems for engineering design and provides a basis for the revision of the similarity function for the adaptation-guided retrieval. However, selecting optimal rule refinements is an unsolved problem in CBR; the employed classical SEEK2-like hill-climbing procedures yield local maxima only, not global ones. Hence for the cased-based optimization of rule base refinement a new operations research approach to the optimal selection of normal, conflicting, and alternative rule refinement heuristics is presented here. As the current rule validation and rule refinement systems usually rely on CBR, this is a relevant novel contribution for coping with the maintenance problem of large CBR systems for engineering design. The described global mathematical optimization enables a higher quality in the case-based refinement of complex engineering rule bases and thereby improves the basis for the adaptation-guided retrieval.