Edwina L. Rissland

CABARET: rule interpretation in a hybrid architecture

Abstract Rules often contain terms that are ambiguous, poorly defined or not defined at all. In order to interpret and apply rules containing such terms, appeal must be made to their previous constructions, as in the interpretation of legal statutes through relevant legal cases. We describe a system CABARET (CAse-BAsed REasoning Tool) that provides a domain-independent shell that integrates reasoning with rules and reasoning with previous cases in order to apply rules containing ill-defined terms. The integration of these two reasoning paradigms is performed via a collection of control heuristics, which suggest how to interleave case-based methods and rule-based methods to construct an argument to support a particular interpretation. CABARET is currently instantiated with cases and rules from an area of income tax law, the so-called “home office deduction”. An example of CABARETs processing of an actual tax case is provided in some detail. The advantages of CABARETs hybrid approach to interpretation stem from the synergy derived from interleaving case-based and rule-based tasks.

Arguments and cases: An inevitable intertwining

We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation. Second, we present,our evolving taxonomy of patterns of actual legal argument. This taxonomy builds upon our much earlier work on ‘argument moves” and also on our more recent analysis of how cases are used to support arguments for the interpretation of legal statutes. Third, we show how the theory of argument used by CABARET, a hybrid case-based/rule-based reasoner, can support many of the argument patterns in our taxonomy.

A case-based system for trade secrets law

In this paper, we give an overview of our case-based reasoning program, HYPO, which operates in the field of trade secret law. We discuss key ingredients of case-based reasoning, in general, and the correspondence of these to elements of HYPO. We conclude with an extended example of HYPO working through a hypothetical trade secrets case, patterned after an actual case.

A case-based approach to modeling legal expertise

Hypo, a computer program that performs case-based reasoning in the legal domain, helps attorneys analyze and make arguments about new fact situations in terms of the most relevant precedent cases. To perform this task, Hypo must make factual comparisons of cases relative to the problem situation and determine the legal significance of comparisons in terms of arguments about the problem situation. The authors describe techniques that Hypo uses to compare cases, choose the best cases for evaluating, and construct arguments about a new fact situation. They demonstrate how Hypo critically compares a problem situation to the most relevantly similar precedent cases to outline an argument regarding how to decide the current fact situation (CFS) based on its significant similarities to and differences from most on point cases (MOPCS). Hypos main tool for this task is the claim lattice mechanism. The authors present a detailed example of a claim lattice actually generated by Hypo to analyze a real legal case.<<ETX>>

BankXX: Supporting legal arguments through heuristic retrieval

The BankXX system models the process of perusing and gathering information for argument as a heuristic best-first search for relevant cases, theories, and other domain-specific information. As BankXX searches its heterogeneous and highly interconnected network of domain knowledge, information is incrementally analyzed and amalgamated into a dozen desirable ingredients for argument (called argument pieces), such as citations to cases, applications of legal theories, and references to prototypical factual scenarios. At the conclusion of the search, BankXX outputs the set of argument pieces filled with harvested material relevant to the input problem situation.This research explores the appropriateness of the search paradigm as a framework for harvesting and mining information needed to make legal arguments. In this article, we describe how legal research fits the heuristic search framework and detail how this model is used in BankXX. We describe the BankXX program with emphasis on its representation of legal knowledge and legal argument. We describe the heuristic search mechanism and evaluation functions that drive the program. We give an extended example of the processing of BankXX on the facts of an actual legal case in BankXXs application domain — the good faith question of Chapter 13 personal bankruptcy law. We discuss closely related research on legal knowledge representation and retrieval and the use of search for case retrieval or tasks related to argument creation. Finally we review what we believe are the contributions of this research to the understanding of the diverse disciplines it addresses.

AI and Similarity

As AI moves into the second half of its first century, we certainly have much to cheer about. For AI to become truly robust, we must further our understanding of similarity-driven reasoning, analogy, learning, and explanation. In this article, the author presents some suggested research directions

AI and law: a fruitful synergy

AI and Law is a classic field for AI research: it poses difficult and interesting problems for AI, and its projects inform both AI and its focal domain, the law itself. This special issue reports on a range of projects, from those where the law motivates fundamental research and whose results reach beyond the legal domain, to those that partake of the benefits of techniques and wisdom from AI as a whole. For instance, projects tackling legal argument have not only created programs that produce legal arguments but also led to insights and advances in the logic of argumentation. Projects with an applications bent have often provided insights about the limitations and nuances of existing techniques, and have served as the catalysts for developing new approaches. For instance, harnessing models of legal argument to teach law students how to argue has led to refinements to and extensions of the models. There is a synergy not only between law and AI, but also between AI and AI and Law. In fact, the work on Case-Based Reasoning (CBR) done in the AI and Law community provided one of the most important streams of results that contributed to the birth of that area in the mid-1980s. Currently, work on legal argumentation is having a similar impact on the international non-standard logic and argumentation communities. AI and Law is much more than an applications area. Its concerns touch upon issues at the very heart of AI: reasoning, representation, and learning. For the AI researcher interested in symbolic methods—or methods of whatever stripe—that are focused on providing explanations and justifications, AI and Law is an excellent arena. No matter how a reasoner arrives at a legal answer it must be explained, justified, compared to and contrasted with alternatives. For the researcher interested in topics like negotiation, decision-making, e-commerce, natural language, information retrieval and extraction, and data mining, AI and Law is a rich source of problems and inspiration.

Finding legally relevant passages in case opinions

Thii paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR.) system, called SPIRE, that locates passages likely to contain information about legally relevant features of cases found in full-text court opinions. SPIRE uses an example base of excerpts from past opinions to form queries, which are run by the INQUERY IR text retrieval engine on individual case opinions. These opinions can be those found by SPIRE in a prior stage of processing, which also employs a hybrid CBR-IR approach to retrieve relevant texts from large document corpora. (This aspect of SPIRE was reported on at ICAIL95.) We present an overview of SPIRE, run through an extended example, and give results comparing SPIRE’s with human performance.

A case-based approach to intelligent information retrieval

We have built a hybrid Case-Based Reasoning (CBR) and Information Retrieval (IR) system that generates a query to the IR system by using information derived from CBR analysis of a problem situation. The query is automatically formed by submitting in text form a set of highly relevant cases, based on a CBR analysis, to a modified version of INQUERY’s relevance feedback module. This approach extends the reach of CBR, for retrieval purposes, to much larger corpora and injects knowledge-based techniques into traditional IR.

Case-based reasoning integrations

This article presents an overview and survey of current work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with model-based reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characterizes the types of multimodal reasoning integrations where CBR can play a role, identifies the types of roles that CBR components can fulfill, and provides examples of integrated CBR systems. Past progress, current trends, and issues for future research are discussed.