, Volume 1, Issue 2-3, pp 113-208

Case-based reasoning and its implications for legal expert systems

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Abstract

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.

Reasoning with cases is important in legal practice of all kinds, and legal practice involves a wide variety of case-based tasks and methods. The paradigms' respective benefits and costs suggest different approaches for different legal tasks.

CBR research and development in the field of AI and Law should be pursued vigoriously for several reasons. CBR can supplement rule-based expert systems, improving their abilities to reason about statutory predicates, solve problems efficiently, and explain their results. CBR can also contribute to the design of intelligent legal data retrieval systems and improve legal document assembly programs. Finally, in cognitive studies of various fields, it can model methods of transforming ill-structured problems into better structured ones through the use of case comparisons.