Toward a computational theory of arguing with precedents
ICAIL '89 Proceedings of the 2nd international conference on Artificial intelligence and law
Inside Case-Based Reasoning
A Case-Based Approach to Modeling Legal Expertise
IEEE Expert: Intelligent Systems and Their Applications
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Although Case-Based Reasoning comes out in order to solve knowledge acquisition bottleneck, a case structure acquisition bottleneck has emerged, superseding it. Because we cannot decide an appropriate case structure in advance, a framework for CBR should be able to improve a case structure dynamically, collecting and analyzing cases. Here is discussed a new framework for knowledge acquisition using CBR and model inference. Model Inference tries to obtain new descriptors(predicates) with interaction of a domain expert, regarding the predicate as the slots that compose a case structure, with an eye to the function of theoretical term generation. The framework has two features: (1) CBR obtains a more suitable group of slots (a case structure) incrementally through cooperation with model inference, and (2) model inference with theoretical term capability discovers the rules which deal with a given task better. Furthermore, we evaluate the feasibility of the framework by implementing it to deal with law interpretation and certify two features with the framework.