Using genetic algorithms to inductively reason with cases in the legal domain

Reasoning pragmatically (rather than using theories of jurisprudence) from cases has been established as a viable model of legal reasoning. Cases are recognized as encapsulating specific knowledge tied to a context. The reasoning done with cases lead to solutions that are tied to situations. To generalize these solutions, the use of machine learning techniques is often necessary. Induction from cases, seen as ‘examples’ with a ‘classification’ in a domain, is one of these techniques. A Genetic Algorithm based approach to inductively learn features of a case in a legal casebase that leads to specified classifications is described in this paper. This knowledge can then be used to reason about cases that are ‘interesting’ by virtue of their features and classification, and to predict classifications of other cases. This method is contrasted with other well known techniques in machine learning. The claim is made that prototgpe exemplars can be generated efficiently and that operational information from any domain (ie. cases) can be used to guide the generation, using variants of this technique.

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