This paper proposes a model ofratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics ofratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model ofratio decidendi fails to (...) account for the dependency of precedential effect on the theory of decision. (shrink)

We provide a retrospective of 25 years of the International Conference on AI and Law, which was first held in 1987. Fifty papers have been selected from the thirteen conferences and each of them is described in a short subsection individually written by one of the 24 authors. These subsections attempt to place the paper discussed in the context of the development of AI and Law, while often offering some personal reactions and reflections. As a whole, the subsections build into (...) a history of the last quarter century of the field, and provide some insights into where it has come from, where it is now, and where it might go. (shrink)

Different legal expert systems may be incompatible with each other: A user in characterizing the same situation by answering the questions presented in a consultation can be led to contradictory inferences. Such systems can be “synthesized” to help users avoid such contradictions by alerting them that other relevant systems are available to be consulted as they are responding to questions. An example of potentially incompatible, related legal expert systems is presented here ‐ ones for the New Jersey murder statute and (...) the celebrated Quinlan case, along with one way of synthesizing them to avoid such incompatibility. (shrink)

The paper identifies and assesses the implications of two approaches to the field of artificial intelligence and legal reasoning. The first — pragmatism — concentrates on the development of working systems to the exclusion of theoretical problems. The second — purism — focuses on the nature of the law and of intelligence with no regard for the delivery of commercially viable systems. Past work in AI and law is classified in terms of this division. By reference to The Latent Damage (...) System, an operational system, the paper articulates and responds to conceivable purist (jurisprudential and AI) objections to such a program. The methods of the pragmatist are also called into question and refined. The author concludes that pragmatism within a purist framework is the only sound approach to developing reliable AI systems in law. (shrink)

In 1880, when Oliver Wendell Holmes (later to be a Justice of the U.S. Supreme Court) criticized the logical theology of law articulated by Christopher Columbus Langdell (the first Dean of Harvard Law School), neither Holmes nor Langdell was aware of the revolution in logic that had begun, the year before, with Frege's Begriffsschrift. But there is an important element of truth in Holmes's insistence that a legal system cannot be adequately understood as a system of axioms and corollaries; and (...) this element of truth is not obviated by the more powerful logical techniques that are now available. (shrink)

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based (...) system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers. (shrink)

This article proposes a formal analysis of a fundamental aspect of legal reasoning: dealing with normative conflicts. Firstly, examples are illustrated concerning the dynamics of legal systems, the application of rules and exceptions, and the semantic indeterminacy of legal sources. Then two approaches to cope with conflicting information are presented: the preferred theories of Brewka, and the belief change functions of Alchourrón, Gärdenfors, and Makinson. The relations between those approaches are closely examined, and some aspects of a model of reasoning (...) with normative conflicts are outlined. Since this model takes into account an ordering of the involved regulations, criteria to order legal norms are finally specified. (shrink)

There is more to legal knowledge representation than knowledge-bases. It is valuable to look at legal knowledge representation and its implementation across the entire domain of computerisation of law, rather than focussing on sub-domains such as legal expert systems. The DataLex WorkStation software and applications developed using it are used to provide examples. Effective integration of inferencing, hypertext and text retrieval can overcome some of the limitations of these current paradigms of legal computerisation which are apparent when they are used (...) on a stand-alone basis. Effective integration of inferencing systems is facilitated by use of a (quasi) natural language knowledge representation, and the benefits of isomorphism are enhanced. These advantages of integration apply to all forms of inferencing, including document generation and casebased inferencing. Some principles for development of integrated legal decision support systems are proposed. (shrink)

Commercial applications of knowledge-based systems are changing from an embryonic to a growth business. Knowledge is classified by levels and types to differentiate various knowledge-based systems. Applications are categorized by size, generic types, and degree of intelligence to establish a framework for discussion of progress and implications. A few significant commercial applications are identified and perspectives and implications of these and other systems are discussed. Perspectives relate to development paths, delivery modes, types of integration, and resource requirements. Discussion includes organizational (...) opportunities and impacts, business perspectives, workplace issues, public effects, and educational needs. Common traps and modes of failures are mentioned.Major conclusions point to the great value of knowledge-based systems when development is pursued correctly, and to the existence of insufficient understanding of resource requirements, needs for integration, different talents, and incorporation into operational strategy to equitably exploit the significant economic and social costs associated with their creation and use. (shrink)

. The current literature in the Artificial Intelligence and Law field reveals uncertainty concerning the potential role of deontic logic in legal knowledge representation. For instance, the Logic Programming Group at Imperial College has shown that a good deal can be achieved in this area in the absence of explicit representation of the deontic notions. This paper argues that some rather ordinary parts of the law contain structures which, if they are to be represented in logic, will call for use (...) of a reasonably sophisticated deontic logic. (shrink)

The purpose of this paper is to improve on the logical and measure-theoretic foundations for the notion of probability in the law of evidence, which were given in my contributions Åqvist [ (1990) Logical analysis of epistemic modality: an explication of the Bolding–Ekelöf degrees of evidential strength. In: Klami HT (ed) Rätt och Sanning (Law and Truth. A symposium on legal proof-theory in Uppsala May 1989). Iustus Förlag, Uppsala, pp 43–54; (1992) Towards a logical theory of legal evidence: semantic analysis (...) of the Bolding–Ekelöf degrees of evidential strength. In: Martino AA (ed) Expert systems in law. Elsevier Science Publishers BV, Amsterdam, North-Holland, pp 67–86]. The present approach agrees with the one adopted in those contributions in taking its main task to be that of providing a semantic analysis, or explication, of the so called Bolding–Ekelöf degrees of evidential strength (“proof-strength”) as applied to the establishment of matters of fact in law-courts. However, it differs from the one advocated in our earlier work on the subject in explicitly appealing to what is known as “Pro-et-Contra Argumentation”, after the famous Norwegian philosopher Arne Naess. It tries to bring out the logical form of that interesting kind of reasoning, at least in the context of the law of evidence. The formal techniques used here will be seen to be largely inspired by the important work done by Patrick Suppes, notably Suppes [(1957) Introduction to logic. van Nostrand, Princeton and (1972) Finite equal-interval measurement structures. Theoria 38:45–63]. (shrink)

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. (shrink)

We shall introduce a set of fundamental legal concepts, providing a definition of each of them. This set will include, besides the usual deontic modalities (obligation, prohibition and permission), the following notions: obligative rights (rights related to other’s obligations), permissive rights, erga-omnes rights, normative conditionals, liability rights, different kinds of legal powers, potestative rights (rights to produce legal results), result-declarations (acts intended to produce legal determinations), and sources of the law.

The Pleadings Game is a normative formalization and computational model of civil pleading, founded in Roberty Alexy''s discourse theory of legal argumentation. The consequences of arguments and counterarguments are modelled using Geffner and Pearl''s nonmonotonic logic,conditional entailment. Discourse in focussed using the concepts of issue and relevance. Conflicts between arguments can be resolved by arguing about the validity and priority of rules, at any level. The computational model is fully implemented and has been tested using examples from Article Nine of (...) the Uniform Commercial Code. (shrink)

This article describes a project which involved an attempt to integrate an expert system with a hypertext database of primary and secondary text materials. Our chosen legal domain was that of the Convention on Jurisdiction and the Enforcement of Judgments in Civil and Commercial Matters (The Brussels Convention 1968). In this article, we address three dimensions of system design. With regard to the legal dimension, we consider the choice of domain and the representation of both knowledge and data in the (...) system. On the technological dimension, we discuss the selection of software development tools and problems associated with keeping knowledge bases and databases up-to-date. Finally, we pay particular attention to the Cinderella dimension of legal expert system development — the user interface. (shrink)

Inspired by legal reasoning, this paper presents a formal framework for assessing conflicting arguments. Its use is illustrated with applications to realistic legal examples, and the potential for implementation is discussed. The framework has the form of a logical system for defeasible argumentation. Its language, which is of a logic-programming-like nature, has both weak and explicit negation, and conflicts between arguments are decided with the help of priorities on the rules. An important feature of the system is that these priorities (...) are not fixed, but are themselves defeasibly derived as conclusions within the system. Thus debates on the choice between conflicting arguments can also be modelled.The proof theory of the system is stated in dialectical style, where a proof takes the form of a dialogue between a proponent and an opponent of an argument. An argument is shown to be justified if the proponent can make the opponent run out of moves in whatever way the opponent attacks. Despite this dialectical form, the system reflects a declarative, or relational approach to modelling legal argument. A basic assumption of this paper is that this approach complements two other lines of research in AI and Law, investigations of precedent-based reasoning and the development of procedural, or dialectical models of legal argument. (shrink)

. This paper offers a survey of the current state of Artificial Intelligence and Law, and makes recommendations for future research. Two main areas of investigation are discussed: the practical work on intelligent legal information systems, and the theoretical work on computational models of legal reasoning. In both areas, the knowledge representation problem is identified as the most important issue facing this field.

It will be argued, firstly, that there is a link between the legal validity of a norm and the rational justifiability of a requirement that judges should apply this norm, based on a normative conception of legal validity and the postulate that judges should act as rational persons; secondly, that rational justifiability of legal norms requires the construction of a legal system in a model of principles that differs from theories, e.g., of Kelsen, Hart, Dworkin and Alexy, which are not (...) fully adequate for a normative conception of law. (shrink)

‘AI & Law’ research has been around since the 1970s, even though with shifting emphasis. This is an overview of the contributions of digital technologies, both artificial intelligence and non-AI smart tools, to both the legal professions and the police. For example, we briefly consider text mining and case-automated summarization, tools supporting argumentation, tools concerning sentencing based on the technique of case-based reasoning, the role of abductive reasoning, research into applying AI to legal evidence, tools for fighting crime and tools (...) for identification. (shrink)

The introduction of results of AI and Law research in actual legal practice advances disturbingly slow. One of the problems is that most research can be classified as either theoretical or pragmatic, while combinations of these two are scarce. This interferes with the need for feedback as well as with the need of getting support, both financially and from actual legal practice. The conclusion of this paper is that an emphasis on research that generates operational and sophisticated systems is necessary (...) in order to provide a future for AI and Law. (shrink)