Modeling Legal Arguments: Reasoning with Cases and Hypotheticals (Artificial Intelligence and Legal Reasoning)

Modeling Legal Argument provides a comprehensive treatment of case-based reasoning and a detailed description of a computer program called Hypo, that models the way attorneys argue with cases, real and hypothetical. The program offers significant advantages over "keyword" case retrieval systems in the legal field and demonstrates how to design expert systems that assist the user by presenting reasonable alternative answers on all sides of an issue and by citing case examples to explain their advice. Hypo analyzes problem situations dealing with trade secrets disputes, retrieves relevant legal cases from its database and fashions them into reasonable legal arguments about who should win. The arguments demonstrate the program's ability to reason symbolically with past cases, to draw factual analogies between cases, to cite them in arguments, to distinguish them, and to pose counter-examples and hypotheticals based on past cases. Modeling Legal Argument discusses the law as a paradigm of case-based argument, introduces Hypo and its adversarial reasoning process, provides an overview of the Hypo program, and gives extended examples of the model's reasoning capabilities. It describes the case knowledge base, a dimensional index, basic mechanisms of case-based reasoning, and offers a theory of case-based argument in Hypo. Ashley evaluates Hypo's performance and takes up adversarial case-based reasoning beyond the law and extensions of the Hypo model. Kevin D. Ashley is a Research Scientist at the Learning Research an Development Center and Assistant Professor of Law at the University of Pittsburgh. Modeling Legal Argument is included in the Artificial Intelligence and Legal Reasoning series, edited by L. Thorne McCarty and Edwina L. Rissland.