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Law, learning and representation
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DOI：10.1016/S0004-3702(03)00109-7.png)
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Abstract
En 中文
In machine learning terms, reasoning in legal cases can be compared to a lazy learning approach in which courts defer deciding how to generalize beyond the prior cases until the facts of a new case are observed. The HYPO family of systems implements a lazy approach since they defer making arguments how to decide a problem until the programs have positioned a new problem with respect to similar past cases. In a kind of reflective adjustment, they fit the new problem into a patchwork of past case decisions, comparing cases in order to reason about the legal significance of the relevant similarities and differences. Empirical evidence from diverse experiments shows that for purposes of teaching legal argumentation and performing legal information retrieval, HYPO-style systems' lazy learning approach and implementation of aspects of reflective adjustment can be very effective. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
legal reasoning
case-based reasoning
lazy learning
legal knowledge representation
legal information retrieval
version spaces
reflective adjustment
argument
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