Legal Reasoning System Research Articles

Analogical lightweight ontology of EU criminal procedural rights in judicial cooperation

This article describes the creation of a lightweight ontology of European Union (EU) criminal procedural rights in judicial cooperation. The ontology is intended to help legal practitioners understand the precise contextual meaning of terms as well as helping to inform the creation of a rule ontology of criminal procedural rights in judicial cooperation. In particular, we started from the problem that directives sometimes do not contain articles dedicated to definitions. This issue provided us with an opportunity to explore a phenomenon typically neglected in the construction of domain-specific legal ontologies. Whether classical definitions are present or absent, laws and legal sources in general are typically peppered with a number of hidden definitions (in the sense that they are not clearly marked out as such) as well as incomplete definitions, which may nevertheless help legal practitioners (and legal reasoning systems) to reason on the basis of analogy or teleology. In this article we describe the theoretical basis for building an analogical lightweight ontology in the framework of an EU project called CrossJustice. We present our methodology for collecting the data, extracting the data fields and creating the ontology with WebProtégé, followed by our conclusions and ideas for future work.

Toward a practical legal rule revision in legal debugging

Computational law researchers have long been interested in developing machine legal reasoning systems and machine-readable rule-bases to simulate legal reasoning given by judges. Basically, legal rule-bases rely on literal interpretations of statutes. In practice, however, literal interpretation of statute can have counterintuitive consequences. To deal with such consequences, English courts (the courts of England and Wales) have developed rules of judicial interpretation for reinterpreting statutes i.e. choosing or modifying the meaning of the statute’s wording (golden rule) or determining the statute’s purpose (mischief rule), and these rules have not been investigated in computational law. Previous research proposed the concept of legal debugging to formalize detection and resolution of counterintuitive consequences in legal rule-bases. Legal debugging consists of two steps. The first step is culprit detection, which involves interacting with a judge in order to identify a culprit, which is intuitively a part of statute that can be determined as a root cause of counterintuitive consequences. The second step is culprit resolution, which involves revising the rule-base to resolve a detected culprit. In this paper, we match two inductive logic programming approaches to two rules of judicial interpretation. The first approach is a bottom-up approach, which involves identifying an exceptional situation of the case and generalizing the legal rule revision. We match the bottom-up approach to the golden rule by using knowledge bases in the form of legal ontologies. The second approach is a top-down approach, which involves determining the purpose of the statute and refining the legal rule revision so that the revision fits within the context of the statute. We match the top-down approach to the mischief rule by using knowledge bases in the form of metarules. By using these matches, legal debugging can perform more practical legal revisions during the culprit resolution step.

Slowing Down AI Legal Reasoning Systems To Befit Slow Paced Adjudication

Slowing Down AI Legal Reasoning Systems To Befit Slow Paced Adjudication

A methodology for designing systems to reason with legal cases using Abstract Dialectical Frameworks

This paper presents a methodology to design and implement programs intended to decide cases, described as sets of factors, according to a theory of a particular domain based on a set of precedent cases relating to that domain. We use Abstract Dialectical Frameworks (ADFs), a recent development in AI knowledge representation, as the central feature of our design method. ADFs will play a role akin to that played by Entity---Relationship models in the design of database systems. First, we explain how the factor hierarchy of the well-known legal reasoning system CATO can be used to instantiate an ADF for the domain of US Trade Secrets. This is intended to demonstrate the suitability of ADFs for expressing the design of legal cased based systems. The method is then applied to two other legal domains often used in the literature of AI and Law. In each domain, the design is provided by the domain analyst expressing the cases in terms of factors organised into an ADF from which an executable program can be implemented in a straightforward way by taking advantage of the closeness of the acceptance conditions of the ADF to components of an executable program. We evaluate the ease of implementation, the performance and efficacy of the resulting program, ease of refinement of the program and the transparency of the reasoning. This evaluation suggests ways in which factor based systems, which are limited by taking as their starting point the representation of cases as sets of factors and so abstracting away the particular facts, can be extended to address open issues in AI and Law by incorporating the case facts to improve the decision, and by considering justification and reasoning using portion of precedents.

An ontology in OWL for legal case-based reasoning

The paper gives ontologies in the Web Ontology Language (OWL) for Legal Case-based Reasoning (LCBR) systems, giving explicit, formal, and general specifications of a conceptualisation LCBR. Ontologies for different systems allows comparison and contrast between them. OWL Ontologies are standardised, machine-readable formats that support automated processing with Semantic Web applications. Intermediate concepts, concepts between base-level concepts and higher level concepts, are central in LCBR. The main issues and their relevance to ontological reasoning and to LCBR are discussed. Two LCBR systems (AS-CATO, which is based on CATO, and IBP) are analysed in terms of basic and intermediate concepts. Central components of the OWL ontologies for these systems are presented, pointing out differences and similarities. The main novelty of the paper is the ontological analysis and representation in OWL of LCBR systems. The paper also emphasises the important issues concerning the representation and reasoning of intermediate concepts.

A hybrid rule --- neural approach for the automation of legal reasoning in the discretionary domain of family law in Australia

Few automated legal reasoning systems have been developed in domains of law in which a judicial decision maker has extensive discretion in the exercise of his or her powers. Discretionary domains c...

AI and Law (2)

In the last issue (Vol.1, No.2), we introduced the Legal Expert System (LES) project led by Hajime Yoshino of Meiji Gakuin University, presenting six papers on the LES project. Those papers were mainly related to higher order reasoning systems such as ase-based reasoning, abductive and inductive logic programming, nonmonotonic reasoning, and analogical reasoning. The objective of the LES project was to develop a legal expert system effective for use by lawyers, so the project covers inference mechanisms, analysis of legal knowledge, and user interfaces. In this second special issue on the LES project, we present five more papers, mainly related to the analysis of legal knowledge, legal knowledge representation language, and legal reasoning system user interfaces. Hajime Yoshino analyzes the logical structure of contract law. To develop a knowledge base for the United Nations Convention on Contracts for the International Sale of Goods (CISG), he proposes a clear logical model of the contract law system, which treats relations between events and legal status such as rights and obligations. Yoshino demonstrates that legal metarules are effective in constructing deductive legal reasoning systems, and are appropriate from the viewpoint of jurisprudence. Seiichiro Sakurai discusses the logical features of the legal knowledge representation language, CPF, developed by Hajime Yoshino. CPF is a logic programming language that enhances the representation of complex data structures. CPF is a convenient tool for representing legal knowledge, yet lawyers often attempt to describe nonexecutable forms of CPF rules.Sakurai introduces a way to construct an executable knowledge base from lawyers' CPF rules. Masato Shibasaki and Katsumi Nitta introduce a new framework to formalize nonmonotonic reasoning with dynamic priorities. The several frameworks proposed thus far to model relationships among arguments do not treat complex arguments, composed of strict rules and default rules. They show that the new framework represents such relationships and analyze these relationships for this framework and others. Takashi Miyata and Yuji Matsumoto introduce LES natural language generation using a user interface for lawyers rather than computer scientists. They describe a sentence generation system that translates logical forms provided from an inference engine into natural-language sentences, and present the unification grammar, generation algorithm and graphical debugging tool. To develop a knowledge base, the lawyers of the LES project analyze and represents the relationships between requirements (actions or events) and consequences (legal status) of legal rules in the form of logical flowcharts. Once the appropriateness of a flowchart is confirmed, they convert it to a CPF rule in their knowledge base. Koji Miyagi, Motoki Miura and Jiro Tanaka developed a flowchart editor that makes legal flowcharting easier. To make it easier to decide where to locate flowchart components and draw linens between the components, the editor possesses several algorithms.

Legal Reasoning Using Abductive Logic Programming

In this paper, we propose a new legal reasoning system using abductive logic programming (ALP). The system can deal with ambiguities of described facts and exceptions which is not described in relevant articles. In addition, the goal, queried to a legal reasoning system, differs in compliance with whether the user is a plaintiff or defendant. In usual deductive legal reasoning systems, there are two major problems in treating legal arguments. One is that legal facts usually have ambiguities, and the other is that two conflicting conclusions must be derived from one knowledge base, depending on whether a plaintiff of defendant is involved. To overcome these difficulties, abductive logic programming is used in our legal reasoning system, which can deal with implicit exceptions and generate presumptions according to the user’s needs.

Special Issue on AI and Law

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

A Symposium on Artificial Intelligence and Research in Japan

This symposium was planned to introduce research activities on AI and Law in Japan. It includes five papers. Research into AI and Law in Japan began in 1980, when Hajime Yoshino (Meiji Gakuin University) started the A-project. Since then, he has recruited several lawyers and computer scientists, and developed some legal reasoning systems which handle the civil code. His research activities gained a high evaluation rating, with the result that the Ministry of Education and Science decided to support his ‘Legal Expert’ project for five years (1993–1998). Currently, about 30 lawyers and 10 computer scientists have joined the project and have developed legal reasoning systems. The main research objective has been to develop logic-based legal knowledge bases. Yoshino selected the United Nations Convention for International Sales of Goods (CISG) as the target domain, to maximise the possibilities for international collaboration. Further research arose from the Fifth Generation Computer Systems Project (1982–1995), which developed a wide range of logic-based parallel processing technologies from hardware to application software. While working for the FGCS project, Katsuma Nitta (Electrotechnical Laboratory and Tokyo Institute of Technology) identified legal reasoning systems as a promising application domain. He organized a Legal Reasoning Research Group consisting of eight researchers, and developed legal reasoning systems. Since the FGCS project ended in 1995, Nitta has been continuing the development of these systems in the Electrotechnical Laboratory and Tokyo Institute of Technology. The papers of this collection were selected from the Legal Reasoning System Workshop held during the International Symposium of FGCS 1994, and revised by appending recent research results. All papers are research results of Yoshino’s ‘Legal Expert’ project or of the FGCS project. Yoshino introduces a logic-based knowledge representation language – Compound Predicate Formula (CPF). He investigated the necessary functions of legal knowledge representation languages and formulated a CPF which has been used in legal reasoning systems. The CPF is a conservative extension of the standard 1st

Similarity of legal cases: From temporal relations of affairs

Case-based reasoning has played an important role in legal reasoning systems. As one criteria for similarity of cases, temporal relationsamong affairs in legal cases should be compared. Thus far in many legalreasoning systems, cases have been described as sequences of pointwiseevents, or at best, simple time intervals, and they have been related bypredicates such as before, after, while,and so on. However, such relations may depend on each implementer'spersonal view, and also require much labor to write down by hand. In this paper, we first propose a classification of affair types by their temporal features, and according to those types, we propose several assumption rules that prescribe the temporal relations between affair types. The temporal relations are automatically generated by these rules. Thereafter, we discuss how thesetemporal relations work in the comparison of similarity of cases. Inthe process of comparison, inadequate temporal relations need to beamended. For this purpose, we introduce revision rules, that refute theresults of assumption rules.

Defeasible reasoning in law

Numerous legal scholars have asserted that legal reasoning is largely a deductive process in which legal rules are applied to the facts of a case in order to derive a conclusion. Though some might contest this assertion, it seems that what we know as law is largely a system of rules. However, these rules are not immutable and fully consistent; in many cases, multiple rules can be applied to the facts of a case, the conclusion derived from one rule conflicting with and potentially defeating that derived from another rule. In this paper, we discuss legal reasoning as defeasible reasoning and present a prototype of a computer-based legal reasoning system to illustrate the advantages of our approach. Defeasible reasoning is an appropriate foundation for the development of legal support systems because it not only can support the non-monotonicity of legal rules but also can resolve many of the conflicts that arise in applying these rules.

Issue spotting in CHASER

For any system that uses previous experience to solve problems in new situations, it is necessary to identify the features in the situation that should match features in the previous cases through some process ofsituation analysis. In this paper, we examine this problem in the legal domain, where lawyers know it asissue spotting. In particular, we present an implementation of issue spotting in CHASER, a legal reasoning system that works in the domain of tort law. This approach is a compromise between generality and efficiency, and is applicable to a range of problems and domains besides legal reasoning. In particular, it presents a principled way to use multiple cases for a single problem by exploiting the inherent structure present in many domains.

HELIC-II: Legal reasoning system on the parallel inference machine

This paper presents HELIC-II, a legal reasoning system on the parallel inference machine. HELIC-II draws legal conclusions for a given case by referring to a statutory law (legal rules) and judicial precedents (old cases). This system consists of two inference engines. The rule-based engine draws legal consequences logically by using legal rules. The case-based engine generates legal concepts by referencing similar old cases. These engines complementally draw all possible conclusions, and output them in the form of inference trees. Users can use these trees as material to construct arguments in a legal suit. HELIC-II is implemented on the parallel inference machine, and it can draw conclusions quickly by parallel inference. As an example, a legal inference system for the Penal Code is introduced, and the effectiveness of the legal reasoning and parallel inference model is shown.

Automated construction of legal arguments

Unlike ordinary expert systems, an automated legal reasoning system does not aim to provide an answer. Its objective should be to provide, for any given case, a well-constructed argument (or preferably several of them) rather than a definitive answer. In this article, some major issues in the design and development of automated legal reasoning systems are discussed. A prototype implementation of an Automated Legal Argument Constructor (ALAC)is presented. This prototype contains a federal act. Uniform Child Custody Jurisdiction ACT (UCCJA), and by examining the evidence, both supporting and disproving, suggests the strongest argument. the article focuses on the foundations on which ALAC was constructed. Our intention is to provide an assessment of the state of the art; however, in some cases, we offer suggestions for new directions.

Representation of legal knowledge for conceptual retrieval

The law is a near perfect application area for knowledge representation. Legal knowledge representation is needed in conceptual legal information retrieval systems and in legal reasoning systems. We review the knowledge representation aspects of four such systems: Waterman and Peterson's Legal Decisionmaking System, Hafner's Legal Information Retrieval System, McCarty's TAXMAN, and the deBessonet representation of the Louisiana Civil Code (CCLIPS).