JACIII Vol.1 No.2 pp. 81-82
doi: 10.20965/jaciii.1997.p0081


Special Issue on AI and Law

Hajime Yoshino and Katsumi Nitta

December 20, 1997
Lawyers use a reasoning process known as legal reasoning to solve legal problems. Legal expert systems could potentially help lawyers solve legal problems more quick and adequately, enable students to study law at school or at home more easily, and help legal scholars and professionals analyze the law and legal systems more clearly and precisely.In 1992, Hajime Yoshino of Meiji Gakuin University started a “Legal Expert Systems” project. This “Legal Expert” project is funded by the Japanese Ministry of Education, Science and Culture and is scheduled to run from May 1992 to March 1998. Yoshino organized over 30 lawyers and computer scientists to clarify legal knowledge and develop legal expert systems.This project covers a wide range of technologies such as the analysis of legal knowledge, the analysis of legal rules on international trade (United Nations Convention on Contracts for International Sale of Goods (CISG)), legal knowledge representation, legal inference models, utility programs to develop legal knowledge bases, and user interfaces. This project, which ends in March 1998, will focus on developing comprehensive legal expert systems as the final product. In this issue, we present 12 papers written by “Legal Expert” project members.In this number, Hajime Yoshino gives are overview of the legal expert systems project, explaining its aims, objectives, and organization. Six papers that follow his introduction include three on case-based reasoning. Legal rules are given by ambiguous predicates, making it difficult sometimes to determine whether conditions for rules are satisfied by the facts given of an event. In such cases, lawyers often refer to old cases and generate hypotheses through analogical reasoning.Kaoru Hirota, Hajime Yoshino and Ming Qiang Xu apply fuzzy theory to case-based reasoning. A number of related systems have been developed, but most focus on qualitative similarities between old cases and the current case, and cannot measure quantitative similarities. Hirota et al. treat quantitative similarity by applying fuzzy theory, explaining their method using CISG examples.Ken Satoh developed a way to compute an interpretation of undefined propositions in a legal rule using adversarial case-based reasoning. He translated old cases giving possible interpretations for a proposition into clauses in abductive logic programming and introduced abducibles to reason dynamically about important factors in an old case to the interpretation suiting the user’s purpose.Yoshiaki Okubo and Makoto Haraguchi formalized a way of attacking legal argument. Assume that an opponent has constructed a legal argument by applying a statute with an analogical interpretation. From the viewpoint of legal stability, the same statue for similar cases should be applied with the same interpretation. We thereby create a hypothetical case similar to the case in question and examine whether the statue can be interpreted analogically. Such a hypothetically similar case is created with the help of a goal-dependent abstraction framework. If a precedent in which a statue has been applied to a case with a different interpretation – particularly complete interpretation – can be found, the opponent’s argument is attacked by pointing out the incoherence of its interpretation of the statue.Takashi Kanai and Susumu Kunifuji proposed a legal reasoning system using abductive logic programming that deals with ambiguities in described facts and exceptions not described in articles. They examined the problems to be solved to develop legal knowledge bases through abductive logic programming, e.g., how to select ambiguities to be treated in abductive reasoning, how to describe time relationships, and how to describe an exception in terms of the application of abductive logic programming to legal reasoning.Toshiko Wakaki, Ken Satoh, and Katsumi Nitta presented an approach of reasoning about dynamic preferences in the framework of circumscription based on logic programming. To treat dynamic preferences correctly is required in legal reasoning to handle metarules such as lex posterior. This has become a hotly discussed topic in legal reasoning and more general nonmonotic reasoning. Comparisons of their method, Brewka’s approach, and Prakken and Sartor’s approach are discussed.Hiroyuki Matsumoto proposed a general legal reasoning model and a way of describing legal knowledge systematically. He applied his method to Japanese Maritime Traffic Law.Six more papers are to be presented in the next number
Cite this article as:
H. Yoshino and K. Nitta, “Special Issue on AI and Law,” J. Adv. Comput. Intell. Intell. Inform., Vol.1 No.2, pp. 81-82, 1997.
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