A general explanation-based learning mechanism and its application to narrative understanding
A general explanation-based learning mechanism and its application to narrative understanding
Concept Learning and Heuristic Classification in Weak-Theory Domains
Concept Learning and Heuristic Classification in Weak-Theory Domains
On the role of prototypes in appellate legal argument (abstract)
ICAIL '91 Proceedings of the 3rd international conference on Artificial intelligence and law
ICAIL '91 Proceedings of the 3rd international conference on Artificial intelligence and law
The pleadings game: formalizing procedural justice
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
A computational model for trial reasoning
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
Representing teleological structure in case-based legal reasoning: the missing link
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
Burden of proof in legal argumentation
ICAIL '95 Proceedings of the 5th international conference on Artificial intelligence and law
An implementation of Eisner v. Macomber
ICAIL '95 Proceedings of the 5th international conference on Artificial intelligence and law
Understanding Similarity: A Joint Project for Psychology,Case-Based Reasoning, and Law
Artificial Intelligence Review
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Precedent-based legal reasoning depends on accurate assessment of relevant similarities between new cases and existing precedents. Determining the relevant similarities between a new case and a precedent with respect to a legal category requires knowing the explanation of the precedent's membership in the category. GREBE is a system that uses both general legal rules and specific explanations of precedents to evaluate legal predicates in new cases. GREBE assesses the similarity of a new case to a precedent of a legal category by attempting to find a pattern of relations in the new case that corresponds to the facts of the precedent responsible for its category membership. Missing relations in the new case are inferred by reusing other explanations from past cases.