Explaining and repairing plans that fail
Artificial Intelligence
Propositional knowledge base revision and minimal change
Artificial Intelligence
A knowledge-intensive, integrated approach to problem solving and sustained learning
A knowledge-intensive, integrated approach to problem solving and sustained learning
Introspective multistrategy learning: on the construction of learning strategies
Artificial Intelligence
Inside Case-Based Reasoning
Experiments On Adaptation-Guided Retrieval In Case-Based Design
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Learning Adaptation Rules from a Case-Base
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Artificial Intelligence - Special issue on nonmonotonic reasoning
Explanation in Case-Based Reasoning---Perspectives and Goals
Artificial Intelligence Review
The Description Logic Handbook
The Description Logic Handbook
Learning adaptation knowledge to improve case-based reasoning
Artificial Intelligence
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Epistemological problems of artificial intelligence
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Learning to integrate multiple knowledge sources for case-based reasoning
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Case base mining for adaptation knowledge acquisition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Engineering and learning of adaptation knowledge in case-based reasoning
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Opportunistic Acquisition of Adaptation Knowledge and Cases -- The IakA Approach
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
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A knowledge-intensive case-based reasoning system has profit of the domain knowledge, together with the case base. Therefore, acquiring new pieces of domain knowledge should improve the accuracy of such a system. This paper presents an approach for knowledge acquisition based on some failures of the system. The cbrsystem is assumed to produce solutions that are consistent with the domain knowledge but that may be inconsistent with the expert knowledge, and this inconsistency constitutes a failure. Thanks to an interactive analysis of this failure, some knowledge is acquired that contributes to fill the gap from the system knowledge to the expert knowledge. Another type of failures occurs when the solution produced by the system is only partial: some additional pieces of information are required to use it. Once again, an interaction with the expert involves the acquisition of new knowledge. This approach has been implemented in a prototype, called FrakaS, and tested in the application domain of breast cancer treatment decision support.