Learning in intractable domains
Machine learning: a guide to current research
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Qualitative Process Theory
Conceptual lattice: a unified model for medical inferece processes
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
An examination of the third stage in the analogy process: verification-based analogical learning
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Towards a model of conceptual knowledge acquisition through directed experimentation
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Conceptual lattice: a unified model for medical inferece processes
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
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In recent years knowledge-based techniques like explanation-based learning, qualitative reasoning and case-based reasoning have been gaining considerable popularity in AI. Such knowledge-based methods face two difficult problems: 1) the performance of the system is fundamentally limited by the knowledge initially encoded into its domain theory 2) the encoding of just the right knowledge to enable the system to function properly over a wide range of tasks and situations is virtually impossible for a complex domain. This paper describes research directed towards the construction of a system that will detect and correct problems with domain theories. This will enable knowledge-based systems to operate with imperfect domain theories and automatically correct the imperfections whenever they pose problems. This paper discusses the classification of imperfect theory problems, strategies for their detection and an approach based on experiment design to handle different types of imperfect theory problems.