Much work on legal knowledge systems treats legal reasoning as arguments that lead from a description of the law and the facts of a case, to the legal conclusion for the case. The reasoning steps of the inference engine parallel the logical steps by means of which the legal conclusion is derived from the factual and legal premises. In short, the relation between the input and the output of a legal inference engine is a logical one. The truth of the (...) conclusion only depends on the premises, and is independent of the argument that leads to the conclusion.This paper opposes the logical approach, and defends a procedural approach to legal reasoning. Legal conclusions are not true or false independent of the reasoning process that ended in these conclusions. In critical cases this reasoning process consists of an adversarial procedure in which several parties are involved. The course of the argument determines whether the conclusion is true or false. The phenomenon of hard cases is used to demonstrate this essential procedural nature of legal reasoning. (shrink)

Law and legal reasoning are a natural target for artificial intelligence systems. Like medical diagnosis and other tasks for expert systems, legal analysis is a matter of interpreting data in terms of higher-level concepts. But in law the data are more like those for a system aimed at understanding natural language: they tell a story about human events that may lead to a lawsuit. Statements of the law, too, are written in natural language and legal arguments are often arguments about (...) what that language means or ought to mean.This study is one of the few research efforts in this fertile area. It is unique in developing a computational model for analyzing legal problems in a way that brings these strands of AI research together and makes sense from a jurisprudential perspective as well.Gardner first analyzes several positions in Anglo-American jurisprudence and their relevance for work in artificial intelligence. She identifies aspects of legal reasoning that any truly expert system in law must make a place for and suggests a way of decomposing the process of legal analysis that takes these aspects into account. She compares the resulting framework with those used by other legal analysis programs. A solid exposition of current AI techniques follows in chapters covering the author's system (written in Maclisp) for offer and acceptance problems, taken from law examinations, involved in contract law.Anne von der Lieth Gardner has a law degree and a Ph.D. in computer science, both from Stanford University. An Artificial Intelligence Approach to Legal Reasoning inaugurates the series Artificial Intelligence and the Law: Processes and Models of Legal Reasoning, edited by L. Thorne McCarty and Edwina L. Rissland. A Bradford Book. (shrink)

The authors describe a logic programming approach to the representation of legislative texts. They consider the potential uses of simple systems which incorporate a single, fixed interpretation of a text. These include assisting in the routine administration of complex areas of the law. The authors also consider the possibility of constructing more complex systems which incorporate several, possibly conflicting interpretations. Such systems are needed for dealing with ambiguity and vagueness in the law. Moreover, they are more suitable than single interpretation (...) systems for helping to test proposed legislation and for helping to give citizens advice. (shrink)