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 report is a description of a new prototype of UC so designed

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...

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.

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Typical natural language interfaces respond passively to the user's commands and queries. They cannot volunteer information, correct user misconceptions, or reject unethical requests. In order to do these things, a system must be an intelligent agent. UC (UNIX Consultant), a natural language system that helps the user solve problems in using the UNIX operating system, is such an intelligent agent. The agent component of UC is UCEgo. UCEgo provides UC with its own goals and plans. By adopting different goals in different situations, UCEgo creates and executes different plans, enabling it to interact appropriately with the user. UCEgo adopts goals from its themes, adopts sub-goals during planning, and adopts meta-goals for dealing with goal interactions. It also adopts goals when it notices that the user either lacks necessary knowledge, or has incorrect beliefs. In these cases, UCEgo plans to volunteer information or correct the user's misconception as appropriate. These plans are pres...

Market forces have fueled a rapid growth in practical systems for legal problem solving. Unfortunately, the AI and Law research community has been relatively disengaged from this process. This paper argues that the AI and Law community could make a larger contribution to practical legal system development by focusing more on task analysis and empirical validation to insure that computational and formal models correspond to actual problem-solving behavior. In particular, the absence of task and corpus analysis has led to models of precedent-based legal reasoning that are inconsistent with actual problem-solving behavior in the AngloAmerican legal system.

The COUNSELOR PROJECT began in the fall of 1984 with the goal of exploring basic problems in discourse structure and text processing within an integrated interface to a strong expert system. The program that we have developed, COUNSELOR, integrates separately developed components for natural language generation (MUMeLE see [7], [8],