Building explanations from rules and structured cases
International Journal of Man-Machine Studies - AI and legal reasoning. Part 1
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Maintaining knowledge about temporal intervals
Communications of the ACM
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Assessing Relevance with Extensionally Defined Principles and Cases
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Case Representation, Acquisition, and Retrieval in SIROCCO
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Assessing the relevance of cases and principles using operationalization techniques
Assessing the relevance of cases and principles using operationalization techniques
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The Role of Information Extraction for Textual CBR
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques
International Journal of Artificial Intelligence in Education
Proceedings of the 13th International Conference on Artificial Intelligence and Law
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Case-based reasoning systems need to know the limitations of their expertise. Having found the known source cases most relevant to a target problem, they must assess whether those cases are similar enough to the problem to warrant venturing advice. In experimenting with SIROCCO, a two-stage case-based retrieval program that uses structural mapping to analyze and provide advice on engineering ethics cases, we concluded that it would sometimes be better for the program to admit that it lacks the knowledge to suggest relevant codes and past source cases. We identified and encoded three strategic metarules to help it decide. The metarules leverage incrementally deeper knowledge about SIROCCO's matching algorithm to help the program "know what it knows." Experiments demonstrate that the metarules can improve the program's overall advice-giving performance.