This paper describes a successful approach to concept learning for heuristic classification. Almost all current programs for this task create or use explicit, abstract generalizations. These programs are largely ineffective for domains with weak or intractable theories. An exemplar-based approach is suitable for domains with inadequate theories but raises two additional problems: determining similarity and indexing exemplars. Our approach extends the exemplar-based approach with solutions to these problems. An implementation of our approach, called Protos, has been applied to the domain of clinical audiology. After reasonable training, Protos achieved a competence level equaling that of human experts and far surpassing that of other machine learning programs. Additionally, an "ablation study" has identified the aspects of Protos that are primarily responsible for its success. 1 Introduction This paper describes a successful approach to the task of concept learning for heuristic clas...

CATO is an intelligent learning environment designed to help beginning law students learn basic skills of making arguments with cases. Using CATO, students practice tasks of induction and analogical argumentation. They practice testing theories against a body of cases and making written arguments about a problem, comparing and contrasting it to past cases. CATO’s model addresses arguments in which two opponents analogize a problem to favorable cases, distinguish unfavorable cases, assess the significance of similarities and differences between cases in light of normative knowledge about the domain, and use that knowledge to organize multi-case arguments. CATO communicates the model to students by presenting dynamically-generated argumentation examples and by reifying argument structure based on the model. CATO also provides a case database and tools based on the model that help make students ’ tasks more manageable. CATO was evaluated in the context of an actual legal writing course, in a study involving 30 first-year law students. We found that instruction with CATO leads to statistically significant improvement in students ’ basic argumentation skills, comparable

. This article surveys the use of logic in computational models of legal reasoning, against the background of a four-layered view on legal argument. This view comprises a logical layer (constructing an argument) ; a dialectical layer (comparing and assessing conicting arguments) ; a procedural layer (regulating the process of argumentation); and a strategic, or heuristic layer (arguing persuasively). Each further layer presupposes, and is built around the previous layers. At the rst two layers the information base is xed, while at the third and fourth layer it is constructed dynamically, during a dialogue or dispute. 1

This paper describes a model of the complementarity of rules and precedents in the classification task. Under this model, precedents assist rule-based reasoning by operationalizing abstract rule antecedents. Conversely, rules assist case-based reasoning through case elaboration, the process of inferring case facts in order to increase the similarity between cases, and term reformulation, the process of replacing a term whose precedents only weakly match a case with terms whose precedents strongly match the case. Fully exploiting this complementarity requires a control strategy characterized by impartiality, the absence of arbitrary ordering restrictions on the use of rules and precedents. An impartial control strategy was implemented in GREBE in the domain of Texas worker's compensation law. In a preliminary evaluation, GREBE's performance was found to be as good or slightly better than the performance of law students on the same task. The Complementarity of Rules and Precedents for ...

: The paper outlines ongoing research on logic-based tools for the analysis and representation of legal contracts, of the kind frequently encountered in large-scale engineering projects and complex, long-term trading agreements. We consider both contract formation and contract performance, in each case identifying the representational issues and the prospects for providing automated support tools. Keywords: Artificial Intelligence and Law; Contract Drafting; Document Assembly; Knowledge Representation. 1. Introduction Over the last twenty years or so a growing body of research in Artificial Intelligence has focussed on the representation of legislation and regulations (for a comprehensive discussion see (Sergot, 1991)). The motivation for this has been twofold: on the one hand there have been opportunities for developing advisory systems for legal practitioners; on the other hand the Law is a complex domain in which diverse modes of reasoning are employed, offering ample opportunity...

. This paper presents a new similarity metric and algorithm for situations represented as graphs. The metric is based on the concept of shared information, and there is discussion of how this would apply for different forms of similarity---including surface, structural and thematic similarity. An algorithm is presented which will determine the similarity of two conceptual graphs for any given measure of information content, which can, as a result, be used for any similarity measure that is based on the concept of shared information. It therefore allows the very flexible use of domain and application specific factors. While the algorithm is not polynomial time, it is argued that for real examples of a useful size it can give an answer in a reasonable time. 1 Introduction This paper addresses the question of measuring the similarity of situations represented as attributed graphs, which includes the use of conceptual graphs. Similarity measurement is an important component in many types ...

Abstract. An overview of a software system under development for “assembling ” legal documents is presented. The system applies Artificial Intelligence (AI) methods and is founded on a theory construction or abduction view of legal reasoning. The AI methods employed include an Assumption-based Truth Maintenance System (ATMS), a Natural Deduction theorem prover, and an implementation of Poole's approach to default reasoning. Legal Document Assembly There has been very little published work on the application of Artificial Intelligence (AI) technology to the problem of constructing legal documents. The bulk of the legal expert systems literature is concerned “only ” with the problem of supporting the task of deciding legal cases, i.e. of identifying and analyzing the legal issues raised by the facts of some case. With the exception of the German KOKON project [Kowalewski 86], where an attempt was made to represent legal rules using Horn clauses, I am aware of no previous attempt to develop an integrated approach to legal analysis and documentation using AI methods. The work described here is one attempt to fill this gap. The usual approach to “assembling ” legal documents is procedural. James Sprowl

This paper argues that the task of matching in case-based reasoning can often be improved by comparing new cases to portions of precedents. An example is presented that illustrates how combining portions of multiple precedents can permit new cases to be resolved that would be indeterminate if new cases could only be compared to entire precedents. A system that uses of portions of precedents for legal analysis in the domain of Texas worker's compensation law, GREBE, is described, and examples of GREBE's analysis that combine reasoning steps from multiple precedents are presented. 1 Introduction A central problem in automated legal reasoning is that many legal predicates lack definitions that provide necessary and sufficient conditions for their satisfaction (McCarty, 1990; Gardner, 1984). Such legal predicates are said to be "opentextured. " Open texture in legal predicates is an instance of the broader phenomenon of category polymorphy (Rosch and Mervis, 1975), the absence of precise...

Most legal expert systems attempt to implement complex models of legal reasoning. Yet the utility of a legal expert system lies not in the extent to which it simulates a lawyer's approach to a legal problem, but in the quality of its predictions and of its arguments. A complex model of legal reasoning is not necessary: a successful legal expert system can be based upon a simplified model of legal reasoning. Some researchers have based their systems upon a jurisprudential approach to the law, yet lawyers are patently able to operate without any jurisprudential insight. A useful legal expert system should be capable of producing advice similar to that which one might get from a lawyer, so it should operate at the same pragmatic level of abstraction as does a lawyer—not at the more philosophical level of jurisprudence. A legal expert system called SHYSTER has been developed to demonstrate that a useful legal expert system can be based upon a pragmatic approach to the law. SHYSTER has a simple representation structure which simplifies the problem of knowledge acquisition. Yet this structure is complex enough for SHYSTER to produce useful advice. SHYSTER is a case-based legal expert system (although it has been designed so that it can be linked with a rule-based system to form a hybrid legal expert system). Its advice is based upon an examination of, and an argument about, the similarities and differences between cases. SHYSTER attempts to model the way in which lawyers argue with cases, but it does not attempt to model the way in which lawyers decide which cases to use in those arguments. Instead, it employs statistical techniques to quantify the similarity between cases. It decides which cases to use in argument, and what prediction it will make, on the basis of that similarity measure. SHYSTER is of a general design: it provides advice in areas of case law that have been specified by a legal expert using a specification language. Four different, and disparate, areas of law have been specified for SHYSTER, and its operation has been tested in each of those legal domains. Testing of SHYSTER in these four domains indicates that it is exceptionally good at predicting results, and fairly good at choosing cases with which to construct its arguments. SHYSTER demonstrates the viability of a pragmatic approach to legal expert system design.

Regulations are a wide-spread and important part of government and business. They codify how products must be made and processes should be performed. Such regulations can be difficult to understand and apply. In an environment of growing complexity of, and change in, regulation, automated support for reasoning with regulations is becoming increasingly necessary. In this paper we report on ongoing work which aims at providing automated support for the drafting and use of regulations using logic modelling techniques.