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

Many researchers have proposed representational schemes to capture complex reasoned discourses. In this paper, we use our experiences with argument representation to examine some of the issues affecting the design of these representational schemes. Our discussions focus on how well a particular scheme, Toulmin structures, maps into the domain of argumentative discourse and captures and highlights various phenomena we consider central to argumentation. We then use this analysis to explore several complementary representational schemes. Finally, we discuss some relatively unexplored factors that influence the usability of these schemes. 1.

Combining the predictions of a set of classifiers has been shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. Increased accuracy has been shown in a variety of real-world applications, ranging from protein sequence identification to determining the fat content of ground meat. Despite such individual successes, the answers are not known to fundamental questions about classifier combination, such as "Can classifiers from any given model class be combined to create a composite classifier with higher accuracy?" or "Is it possible to increase the accuracy of a given classifier by combining its predictions with those of only a small number o...

In this paper we put forward a formal description of theories which can be used to record understanding of, and explain decisions in, case law domains. We believe that reasoning with cases involves all of theory construction, use and evaluation, and that awareness of the theory which provides a context for case based arguments is essential to understanding such arguments. Moreover, our account of these theories includes a systematic link between factors and values, which we believe is necessary to explain why some arguments prove to be more persuasive than others. We begin by formalising the various elements that the theories contain, and then provide a set of theory constructors which allow theories to be built up from the background of decided cases. We show how such theories can be used to explain decisions on particular cases. We discuss how theories can be compared and evaluated. We then show how the argument moves of HYPO and CATO can be understood in terms of our framework. We conclude with a brief discussion of an implementation of the framework, and a summary of the major features of our approach. 1.

Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference -- hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples -- can provide a complete picture of how people generalize concepts in even this simple setting. This thesis prop...

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.

Most work in artificial intelligence and law has concentrated on modelling the type of reasoning done by trial lawyers. In fact, most lawyers' work involves planning -- for example, wills and trusts, real estate deals, and business mergers and acquisitions. Certain planning issues, such as the use of underspecified, or "open-textured" rules, are illustrated especially clearly in this domain. In this thesis, I set forth the characteristic features of planning in law, place it in the context of past artificial intelligence work in both law and planning, and describe CHIRON, a system that I have developed implementing my theory of open-textured planning in the domain of personal income tax law.

Abstract. Case-based Reasoning (CBR) began as a theory of human cognition, but has attracted relatively little direct experimental or theoretical investigation in psychology. However, psychologists have developed a range of instance-based theories of cognition and have extensively studied how similarity to past cases can guide categorization of new cases. This paper considers the relation between CBR and psychological research, focussing on similarity in human and artificial case-based reasoning in law. We argue that CBR, psychology and legal theory have complementary contributions to understanding similarity, and describe what each offers. This allows us to establish criteria for assessing existing CBR systems in law and to establish what we consider to be the crucial goals for further research on similarity, both from a psychological and a CBR perspective.

In this paper we explore the trend towards the production of "ontologies" as part of the development of knowledge based systems, both in general AI and in the legal domain in particular. We discuss four examples of this kind of work in the legal domain and identify areas on which future work might be directed. Introduction Since their introduction, knowledge based and expert systems have attracted a mixed reaction, mingling excitement at their potential with dissatisfaction with their various limitations. This has been true both in general AI, and in the legal field. Much work has therefore concentrated on understanding the limitations, and developing methods for building the systems which can mitigate the limitations. The overall trend has been away from seeing the process as one of encoding heuristics derived from an expert, towards modelling the domain on which the expertise operates. Further it has become recognised that modelling requires as a precondition that the domain be con...

Formulating hypotheses about natural phenomena and testing them against empirical data have long been cornerstones of the natural sciences. As a cognitive framework, hypothesis formation and testing also play important roles in mathematical discovery and in legal reasoning, especially as illustrated in oral arguments before the United States Supreme Court. A hypothesis is a tentative assumption made in order to draw out and test its normative, logical or empirical consequences. A hypothetical is an imagined situation that involves a hypothesis; it is a tool for drawing out those consequences. In Supreme Court oral arguments, the hypotheses are an advocate’s proposed test or standard for deciding a case. The Justices pose hypotheticals to probe the advocates ’ tests, assessing their meaning, consistency with past decisions, and their legal and policy implications. In challenging a proposed test by posing hypotheticals, the Justices sometimes induce the advocate to modify or abandon the hypothesis. This paper presents a model of the role of hypotheticals in assessing legal hypotheses and illustrates it with examples drawn from actual Supreme Court oral arguments. A study of these examples and of jurisprudential models has led to a more complete schematization and model of the process of framing and testing legal hypotheses for purposes of designing an Intelligent Tutoring System. The paper will introduce a new approach to help law students understand the interpretive role of hypothetical reasoning in legal argument, a computerized collaborative instructional environment for graphically marking up and reflecting upon the relationships of proposed tests, hypothetical challenges, and responses like distinguishing the hypothetical or modifying the test.