"Legal Expert" Project

Informatics for the Law: LEXIS—A legal expert system on Italian family law

Legal reasoning involves case analysis in statutory as well as real world perspectives. The impact of real world perspective on case analysis poses a serious challenge to knowledge engineers for building legal expert systems. A legal expert system intends to provide intelligent support to legal professionals. The proposed legal predictive system is an attempt to predict the most probable outcome of a case according to statutory as well as real world knowledge of the legal domain. The system accepts the current fact situation of a case and analyses it interactively with legal personnel. This work introduces a frame-like knowledge structure,lattice, with two-dimensional attributes. This paper contains a detailed discussion on artificial intelligence-based case analysis of theft cases in a real world perspective.

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

Top-Down and Bottom-Up Legal System as a Gödel Machine With Object-Oriented Framework

Based on the interpretation on the evolution of theoretical basis of the construction of legal expert system as the clue, this article mainly analyzes and points out the dilemma faced by symbolism and connectionism as the algorithm on construction of legal expert system. Through defeasible reasoning, a contextual-based legal expert system could be realized. The legal expert system should be constructed with the judge as the center character, in which the personality of the judge could not necessarily be eliminated. As a result, the article provides a model of a legal expert system based on dual processing, including factual argumentation and legal argumentation.

This paper presents a critical analysis of a legal expert system architecture, focusing the knowledge representation that represents the most important step in the construction of a modern legal information system. After a long period of experience, we can speak of global systems, because the formalized system knowledge is only a part of system data that may be improved to the user also from a periferic data bases. The new legal systems are tools that can offer information for decision aid and user consulting from different modules and also they offer a sophisticated information retrieval founded, for instance, upon hypertext models or thesauri.

Despite growing needs of the legal artificial intelligence (AI), its development is slower than other AI domains because legal expertise is essentially required to develop legal AI systems. Legal knowledge representation on legal expertise needs to be considered to implement legal reasoning AI systems. In this paper, we present a legal reasoning methodology, which utilizes multiple expert knowledge based agents. These agents are designed to solve recognizing textual entailment (RTE) problems with syntactic and interpretative knowledge. The validity of the proposed method is provided through experiments with the COLIEE 2017 data.

Semantics is Central to Legal ReasoningThe consensus among legal philosophers is probably that rule-based legal expert systems leave much to be desired as aids in legal decision-making.Why?What can we do about it?A bureaucrat administering some set of complex rules will ascertain the facts and apply the rules to them in order to discover their consequences for the case in hand.This process of deductive reasoning is characteristically bureaucratic.'If the client or subject of the decision, after he has checked the deductions, does not like the apparent consequences of the rules, he will question their interpretation.This is not a deductive process.He will examine the meanings of the words in the rules and those used to characterise his case, looking for adjustments that lead to a more favourable decision.

The Datalex Project: History and Bibliography

A legal expert system expresses as a set of formal rules the norms found in the provisions of a statute or regulation, in case law or other legal texts. The process of constructing the system involves interpreting these legal texts and recasting each of them into one or more formal legal rules. The primary sources of law or law-formulations, to use Susskind’s term [Susskind 87, 36-37, 124], must be transformed into law-statements—statements about what the content of the law is—and these in turn must be translated into a formal language as rules of inference which Susskind terms legal productions. These transformations take place outside the context of specific cases, that is without reference to concrete legal problems those rules are designed to solve. The construction of a legal expert system involves two conceptual steps: to identify the legal norms that a statute conveys and to express these norms as formal rules. Several strategies have been proposed for this transformation process. Some consist of a rather straightforward transposition of law texts into formal language at the expense of a substantial loss of meaning of the legal concepts involved; others call for a subtler and more complex legal analysis. In all cases, however, what is at stake is the interpretation of legal documents. The problem of interpreting legal documents is well known to lawyers. In legal usage, the term interpretation is employed when the meaning of a legal text, typically a statutory provision, has to be assessed in a concrete situation. In constructing expert systems, one must interpret legal documents ahead of such concrete applications. This interpretation can only be provisional. The best one can do in constructing the knowledge base is to foreclose as few as possible of the meanings for particular provisions one may ultimately want to consider in concrete situations. Conversely, when using the system in a particular situation, one may want to consider different interpretations and their

This paper presents a four layer model for working with legal knowledge in expert systems. It distinguishes five sources of knowledge. Four contain basic legal knowledge found in published and unpublished sources. The fifth consists of legal metaknowledge. In the model the four basic legal knowledge sources are placed at the lowest level. The metaknowledge is placed at levels above the other four knowledge sources. The assumption is that the knowledge is represented only once. The use of metaknowledge at various levels should make it possible to use the appropriate knowledge for the problem presented to the system. The knowledge has to be represented as closely to the original format as possible for this purpose. Suitable representation formalisms for the various types of knowledge in the five knowledge sources are discussed. It is not possible to indicate a `best' representation formalism for each knowledge source.

Legal experts can reliably ascertain which legal norms are valid, and therefore which legal propositions are true. Legal experts are in this position because they have a grasp of the deep cognitive structure of law: the habits, commitments, and values that characterize the practice of legal argument in a specific jurisdiction. Legal expertise, thus understood, determines the content of the law, which is therefore not reducible to an aggregation of authoritative legal texts. Because social facts about legal experts’ values and commitments can determine the content of the law, references to moral considerations and principles of justice as valid legal reasons, in legal argument, are not a problem for (at least some forms of) legal positivism. Legal expertise thus shows that non-positivists might be right about (as they would put it) the irreducibility of legal content to communicative content without being right about the claims that moral facts determine the content of the law or that the validity of legal norms turns on moral considerations. Legal expertise also plays an important causal role in legal decision-making and is not, as some of the literature on judicial decision-making might suggest, epiphenomenal. From a moral perspective, while the existence of a class of legal experts is not necessarily valuable, it does have certain benefits, particularly in conditions of moral disagreement.

This paper deals with discourse analysis, with specific reference to the Linguistic and Logic Based Legal Expert System, LEX. In the LEX project we concentrated on a few arbitrarily selected court decisions, extracted the case descriptions, and then added the necessary background knowledge to our prototype expert system to analyze the case descriptions and to deduce the answers to some juridical questions. In this paper we present and comment on a typical discourse representation structure for an accident description in the corpus we studied.

Integration of computing systems and methods in legal activity allows to extract benefits such as resource saving, increase objectivity, completeness and accuracy of intellectual results. Understanding of the main scientific achievements and trends at the intersection of computer and legal sciences focuses on promising scientific and technological areas of informatization of law. This review systematizes the most important achievements at the intersection of legal and computer sciences, covering foreign and domestic scientific publications for 1949–2020. Researches of computer methods and systems initiated by American jurimetrics were aimed at storing, indexing, abstracting and searching for legal texts and led to the creation of legal information retrieval systems. Domestic legal cybernetics became a pioneer in the field of automation of criminalistics expert examination. Computer modeling of legal reasoning developed on the techniques of artificial intelligence, later it shifted to the field of legal dialogue and conflict of legal arguments, but in recent years it transforms the acquired experience on the basis of modern computer models, patterns and architectures. The popularization of decision-making support systems has provided multi-tasking systems, including information retrieval, legal reasoning, analytics, predictions and control. The latest research segments are focused on the application of machine learning and big data processing. Almost all successful methodological solutions retain their significance, continuing to be applied directly or as the basis for the further development of computational methods and information systems in legal activity.

The contemporary development of deontic logic since von Wright has been based on the study of the analogies between normative and alethic modalities. The weakest deontic logic called standard deontic logic (SDL) is the modal system of type KD. Jones and Sergot argued that contrary-to-duty (CTD) reasoning was necessary to represent the legal codes in legal expert systems. This reasoning invites such CTD paradoxes as Chisholm’s Paradox of SDL that is monadic. Hansson’s dyadic deontic logic can avoid CTD paradoxes. But it introduces such dilemmas as the Considerate Assassin’s Dilemma. Prakken and Sergot, and van der Torre and Tan proposed preference-based dyadic deontic logics that can explain away this dilemma. However, these logics face the Fundamental Problem of Intrinsic Preference. The aim of this paper is to propose a new non-modal logical version of complete and decidable preference-based dyadic deontic logic–conditional expected utility maximiser’s deontic logic (CEUMDL) that can avoid Chisholm’s Paradox and explain away the Considerate Assassin’s Dilemma. In the model of CEUMDL we can explain an agent’s preferences in terms of his degrees of belief and degrees of desire via conditional expected utility maximisation, which can avoid the Fundamental Problem of Intrinsic Preference and furnish a solution to the Gambling Problem. We provide CEUMDL with a Domotor-type model that is a kind of measurement-theoretic and decision-theoretic one.Keywordsdeontic logicpreferencemeasurement theoryrepresentation theoremconditional expected utility maximisationprojective geometry