Case-based reasoning and its implications for legal expert systems

Artificial Intelligence and Law 1 (2-3):113-208 (1992)
Reasoners compare problems to prior cases to draw conclusions about a problem and guide decision making. All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases. Across domains and tasks, however, humans reason with cases in subtly different ways evidencing different mixes of and mechanisms for these components.In recent CBR research in Artificial Intelligence (AI), five paradigmatic approaches have emerged: statistically-oriented, model-based, planning/design-oriented, exemplar-based, and adversarial or precedent-based. The paradigms differ in the assumptions they make about domain models, the extent to which they support symbolic case comparison, and the kinds of inferences for which they employ cases.
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DOI 10.1007/BF00114920
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References found in this work BETA
Edward Levi (1951). An Introduction to Legal Reasoning. Journal of Philosophy 48 (5):167-168.

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Citations of this work BETA
Jaap Hage (2004). Comparing Alternatives in the Law. Artificial Intelligence and Law 12 (3):181-225.
Henry Prakken & Giovanni Sartor (1996). Editors' Introduction. Artificial Intelligence and Law 4 (3-4):157-161.

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Jaap Hage (2004). Comparing Alternatives in the Law. Artificial Intelligence and Law 12 (3):181-225.
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