Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Automatic knowledge base refinement for classification systems
Artificial Intelligence
Learning flexible concepts from streams of examples: FLORA2
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Learning in the presence of concept drift and hidden contexts
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
TRACKING DRIFTING CONCEPTS BY MINIMIZING DISAGREEMENTS
TRACKING DRIFTING CONCEPTS BY MINIMIZING DISAGREEMENTS
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Tracing actions helps in understanding interactions
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Multi-context systems with activation rules
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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The paper focuses on a difficult problem when formalizing knowledge: What about the possible concepts that didn't make it into the formalization? We call such concepts the unconsidered context of the formalized knowledge and argue that erroneous and inadequate behavior of systems based on formalized knowledge can be attributed to different states of the unconsidered context; either while formalizing or during application of the formalization. We then propose an automatic strategy to identify different states of unconsidered context inside a given formalization and to classify which parts of the formalization to use in a given application situation. The goal of this work is to uncover unconsidered context by observing sucess and failure of a given system in use. The paper closes with the evaluation of the proposed procedures in an error diagnosis scenario featuring a plan based user interface.