Deductive, inductive and analogical reasoning in legal decision support systems

In this paper we provide an overview of a number of fundamental reasoning formalisms in artificial intelligence which can and have been used in modelling legal reasoning. We describe deduction, induction and analogical reasoning formalisms, and show how they can be used separately to model legal reasoning. We argue that these formalisms can be used together to model legal reasoning more accurately, and describe a number of attempts to integrate the approaches

Applied legal knowledge based systems

For many years researchers have been working in the intersection between artificial intelligence and law. The first International Conference on Artificial Intelligence and Law was held in 1987, but prior to this there were many individual researchers who found this a fertile and interesting field in which to examine the nature of both law and artificial intelligence techniques. The systems described show that it is possible and desirable to build useful applied systems in law. They show that they can have positive benefits in people's lives, and that the technology exists to create worthwhile systems. They also point to some general principles in designing rule-based systems in legal domains. However, they equally show that legal reasoning is harder than it looks, and that we still have significant work ahead of us if we are to build easy-to-use, applied legal knowledge based systems.</p

Rationales for the continued development of legal expert systems

This article looks at the development and enormous potential of legal expert systems and the problems which often arise due to a suspicious and ill-informed legal fraternity. As most lawyers find themselves uncomfortable with machines generally, and computers specifically, being used to writing, reading and analysing human problems, and have little contact with technology, except where it affects a client or can be used to improve the efficiency of their practices, it is not hard to see that in more advanced uses of computer technology lawyers still struggle to understand fundamental concepts. Such an example is Artificial Intelligence ('AI') and Expert Systems ('ESs'). This article seeks to address some of the misconceptions which the legal profession apparently has about Al and its application to the Law. The area of AI and Law has not been sufficiently explained to legal practitioners and academics, a situation which this article hopes to address, along with providing a useful overview of the field

Engineering management journal : EMJ

\ud \ud In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies

Beyond rule based reasoning - the meaning and use of cases

Commercial legal expert systems are invariably rule based. Such systems are poor at dealing with open texture and the argumentation inherent in law. To overcome these problems we suggest supplementing rule based legal expert systems with case based reasoning or neural networks. Both case based reasoners and neural networks use cases-but in very different ways. We discuss these differences at length. In particular we examine the role of explanation in existing expert systems methodologies. Because neural networks provide poor explanation facilities, we consider the use of Toulmin argument structures to support explanation (S. Toulmin, 1958). We illustrate our ideas with regard to a number of systems built by the author

Designing intelligent litigation support tools : The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain

Developing Co-operating Legal Knowledge Based Systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work supplements rule-based reasoning with case based reasoning and intelligent information retrieval. This research, specifies an approach to the case based retrieval problem which relies heavily on an extended object-oriented / rule-based system architecture that is supplemented with causal background information. Machine learning techniques and a distributed agent architecture are used to help simulate the reasoning process of lawyers. In this paper, we outline our implementation of the hybrid IKBALS II Rule Based Reasoning / Case Based Reasoning system. It makes extensive use of an automated case representation editor and background information