Abstract
This article reviews computational models of legal reasoning as they are being developed in the field of artificial intelligence and law. A theoretical aim of these models is to understand legal reasoning by simulating it in a computer program, while a practical aim is to study how advanced information technology can aid legal practice. First logical deduction is discussed as a necessary but insufficient component of realistic models of legal reasoning. Then models of defeasible legal reasoning and legal argumentation are discussed, which focus on the generation and comparison of reasons or arguments for and against legal claims. Computational models of legal interpretation emphasize the interplay between rules and cases and the role of principles, purposes, and values. Computational models of legal proof account for the uncertainty in legal proof in three alternative ways: with Bayesian probability theory, argumentation, and narrative. Finally, procedural models of legal reasoning are based on the idea that the quality of a legal decision partly depends on how it was reached.
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