Acquiring search-control knowledge via static analysis
Artificial Intelligence
Representing problem-solving for knowledge refinement
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Knowledge Refinement for a Design System
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
Using Ontologies for Defining Tasks, Problem-Solving Methods and their Mappings
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
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Knowledge refinement tools seek to correct faulty knowledge based systems (KBSs). Most current refinement systems are applicable only to a single KBS shell, and typically they ignore the procedural aspects of KBS reasoning. This paper describes the KrustWorks framework which refines a number of different shells, and can be extended to new ones. Internal knowledge structures represent rules in the target KBS and their interactions, and generic tools manipulate these structures. In this paper KrustWorks is evaluated on two aero-space applications into which various artificial faults have been introduced. KRUSTWorks identifies and fixes these faults, except when the training examples provide insufficient fault evidence. The evaluation demonstrates the effectiveness of KrustWorks as a refinement tool, and confirms that it can represent the knowledge and problem-solving in real expert systems.