The problem of reasoning by case analysis

This article is the twenty-ninth of a series of articles discussing various open research problems in automated reasoning. The problem proposed for research asks one to find criteria that an automated reasoning program can apply to decide when to conduct a case analysis argument and to decide which cases are appropriate. When the choice of employing a case analysis argument is wise and the cases are well chosen, the likelihood that the reasoning program — or, for that matter, a person — will find an answer to the given question is sharply increased.This work was supported by the Office of Scientific Computing, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

An AI model of case-based legal argument from a jurisprudential viewpoint

This article describes recent jurisprudential accountsof analogical legal reasoning andcompares them in detail to the computational modelof case-based legal argument inCATO. The jurisprudential models provide a theoryof relevance based on low-levellegal principles generated in a process ofcase-comparing reflective adjustment. Thejurisprudential critique focuses on the problemsof assigning weights to competingprinciples and dealing with erroneously decidedprecedents. CATO, a computerizedinstructional environment, employs ArtificialIntelligence techniques to teach lawstudents how to make basic legal argumentswith cases. The computational modelhelps students test legal hypotheses againsta database of legal cases, draws analogiesto problem scenarios from the database, andcomposes arguments by analogy with a setof argument moves. The CATO model accountsfor a number of the important featuresof the jurisprudential accounts, includingimplementing a kind of reflective adjustment.It also avoids some of the problems identifiedin the critique; for instance, it deals withweights in a non-numeric, context-sensitivemanner. The article concludes by describingthe contributions AI research can make tojurisprudential investigations of complexcognitive phenomena of legal reasoning. Forinstance, unlike the jurisprudential models,CATO provides a detailed account of how togenerate multiple interpretations of a citedcase, downplaying or emphasizing the legalsignificance of distinctions in terms of thepurposes of the law as the argument contextdemands.     

The problem of induction

This article is the thirtieth of a series of articles discussing various open research problems in automated reasoning. The problem proposed for research asks for criteria for accurately determining when an induction argument is the appropriate form of argument for an automated reasoning program to employ. This research problem also asks for criteria for choosing well the property on which to conduct the induction argument.This work was supported by the Office of Scientific Computing, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

Uncertain reasoning about agents' beliefs and reasoning

Reasoning about mental states and processes is important in varioussubareas of the legal domain. A trial lawyer might need to reason andthe beliefs, reasoning and other mental states and processes of membersof a jury; a police officer might need to reason about the conjecturedbeliefs and reasoning of perpetrators; a judge may need to consider adefendant's mental states and processes for the purposes of sentencing;and so on. Further, the mental states in question may themselves beabout the mental states and processes of other people. Therefore, if AIsystems are to assist with reasoning tasks in law, they may need to beable to reason about mental states and processes. Such reasoning isriddled with uncertainty, and this is true in particular in the legaldomain. The article discusses how various different types ofuncertainty arise, and shows how they greatly complicate the task ofreasoning about mental states and processes. The article concentrates onthe special case of states of belief and processes of reasoning, andsketches an implemented, prototype computer program (ATT-Meta) thatcopes with the various types of uncertainty in reasoning about beliefsand reasoning. In particular, the article outlines the system'sfacilities for handling conflict between different lines of argument,especially when these lie within the reasoning of different people. Thesystem's approach is illustrated by application to a real-life muggingexample.     

Evaluating a Legal Argument Program: The BankXX Experiments

In this article we evaluate the BankXX program from several perspectives. BankXX is a case-based legal argument program that retrieves cases and other legal knowledge pertinent to a legal argument through a combination of heuristic search and knowledge-based indexing. The program is described in detail in a companion article in Artificial Intelligence and Law 4: 1--71, 1996. Three perspectives are used to evaluate BankXX:(1) classical information retrieval measures of precision and recall applied against a hand-coded baseline; (2) knowledge-representation and case-based reasoning, where the baseline is provided by the functionality of a well-known case-based argument program, HYPO (Ashley, 1990); and (3) search, in which the performance of BankXX run with various parameter settings, for instance, resource limits, is compared. In this article we report on an extensive series of experiments performed to evaluate the program. We also describe two additional experiments concerning(1) the program's search behavior; and (2) the use of a modified form of precision and recall based on case similarity. Finally we offer some general conclusions that might be drawn from these particular experiments.     

Artificial argument assistants for defeasible argumentation

The present paper discusses experimental argument assistance tools. In contrast with automated reasoning tools, the objective is not to replace reasoning, but to guide the user's production of arguments. Two systems are presented, and based on . The focus is on defeasible argumentation with an eye on the law. Argument assistants for defeasible argumentation naturally correspond to a view of the application of law as dialectical theory construction. The experiments provide insights into the design of argument assistants, and show the pros and cons of different ways of representing argumentative data. The development of the argumentation theories underlying the systems has culminated in the logical system that formalizes the interpretation of prima facie justified assumptions. introduces an innovative use of conditionals expressing support and attack. This allows the expression of warrants for support and attack, making it a transparent and flexible system of defeasible argumentation.     

Practical reasoning as presumptive argumentation using action based alternating transition systems

In this paper we describe an approach to practical reasoning, reasoning about what it is best for a particular agent to do in a given situation, based on presumptive justifications of action through the instantiation of an argument scheme, which is then subject to examination through a series of critical questions. We identify three particular aspects of practical reasoning which distinguish it from theoretical reasoning. We next provide an argument scheme and an associated set of critical questions which is able to capture these features. In order that both the argument scheme and the critical questions can be given precise interpretations we use the semantic structure of an Action-Based Alternating Transition System as the basis for their definition. We then work through a detailed example to show how this approach to practical reasoning can be applied to a problem solving situation, and briefly describe some other previous applications of the general approach. In a second example we relate our account to the social laws paradigm for co-ordinating multi-agent systems. The contribution of the paper is to provide firm foundations for an approach to practical reasoning based on presumptive argument in terms of a well-known model for representing the effects of actions of a group of agents.     

Argument in Artificial Intelligence and Law

In this paper I shall discuss the notion of argument, and the importanceof argument in AI and Law. I shall distinguish four areas where argument hasbeen applied: in modelling legal reasoning based on cases; in thepresentation and explanation of results from a rule based legal informationsystem; in the resolution of normative conflict and problems ofnon-monotonicity; and as a basis for dialogue games to support the modellingof the process of argument. The study of argument is held to offer prospectsof real progress in the field of AI and law, and the purpose of this paperis to provide an overview of work, and the connection between the various strands.     

ASHSD-II: A computational model for litigation support

Abstract: In this paper a hybrid knowledge-based system which exploits both rule-based reasoning (RBR) and case-based reasoning (CBR) is presented. The issues of RBR and CBR in general in the context of legal knowledge-based systems and legislation in rule form and previously-decided cases in an interconnected graph form are discussed. It is possible for the user to select either reasoning method (RBR or CBR), or indicate no preference. The rule base of this system consists of two types of rule. The first type of rule determines which options are legally applicable. The second type indicates how the courts are likely to act within the range of options available, which is determined by the first type of rule. When CBR is selected, the system uses the features of previously-decided cases to select the most similar cases to the situation that is described in the input and displays their details of decisions. In case of the selection of no preference option, the system applies RBR and CBR method separately, and then presents results based on an automated relative rating of the qualities of the RBR (based on the second type of rules) and CBR advice. These ideas have been implemented in a prototype system, known as A dvisory S upport for H ome S ettlement in D ivorce (ASHSD-II).     

A model of argumentation and its application to legal reasoning

We present a computational model of dialectical argumentation that could serve as a basis for legal reasoning. The legal domain is an instance of a domain in which knowledge is incomplete, uncertain, and inconsistent. Argumentation is well suited for reasoning in such weak theory domains. We model argument both as information structure, i.e., argument units connecting claims with supporting data, and as dialectical process, i.e., an alternating series of moves by opposing sides. Our model includes burden of proof as a key element, indicating what level of support must be achieved by one side to win the argument. Burden of proof acts as move filter, turntaking mechanism, and termination criterion, eventually determining the winner of an argument. Our model has been implemented in a computer program. We demonstrate the model by considering program output for two examples previously discussed in the artificial intelligence and legal reasoning literature.     

Is there a Burden of Questioning?

In some recent cases in Anglo-American law juries ruled contrary to an expert's testimony even though that testimony was never challenged, contradicted or questioned in the trial. These cases are shown to raise some theoretical questions about formal dialogue systems in computational dialectical systems for legal argumentation of the kind recently surveyed by Bench-Capon (1997) and Hage (2000) in this journal. In such systems, there is a burden of proof, meaning that if the respondent questions an argument, the proponent is obliged to offer some support for it give it up. But what should happen in a formal system of dialogue if the proponent puts forward an argument and the respondent fails to critically question it, and simply moves on to another issue? Is this some kind of fault that should have implications? Should it be taken to imply that, by default, the respondent has conceded the argument? What, if anything, should be the outcome of such a failure to question in a formal dialogue system of argumentation? These questions are considered by examining some legal cases of expert opinion testimony in relation rules for formal dialectical argumentation systems.     

Using argument schemes for hypothetical reasoning in law

This paper studies the use of hypothetical and value-based reasoning in US Supreme-Court cases concerning the United States Fourth Amendment. Drawing upon formal AI & Law models of legal argument a semi-formal reconstruction is given of parts of the Carney case, which has been studied previously in AI & law research on case-based reasoning. As part of the reconstruction, a semi-formal proposal is made for extending the formal AI & Law models with forms of metalevel reasoning in several argument schemes. The result is compared with Rissland’s (1989) analysis in terms of dimensions and Ashley’s (2008) analysis in terms of his process model of legal argument with hypotheticals.     

Resolution for Skeptical Stable Model Semantics

An extension of resolution for skeptical stable model semantics is introduced. Unlike previous approaches, our calculus is not derived from credulous inference and enjoys a number of properties that are not satisfied by current nonmonotonic reasoning systems. Skeptical resolution is top down, in general, and goal directed on call-consistent programs. It does not need the given program to be instantiated before reasoning. It may compute nonground answer substitutions efficiently. It is compatible with different implementations of negation as failure. Some inferences, which depend on nonground negative goals, can be drawn without resorting to negation-as-failure; as a consequence, many goals that flounder in the standard setting have a successful skeptical derivation. The paper contains a preliminary study of some interesting derivation strategies and a sketch of a prototype implementation of the calculus.     

An argumentation framework in default logic

This article presents a formal theory about nontrivial reasoning with inconsistent information, applicable, among other things, to defeasible reasoning. The theory, which is inspired by a formal analysis of legal argument, is based on the idea that inconsistency tolerant reasoning is more than revising an unstructural set of premises; rather it should be regarded as constructing and comparing arguments for incompatible conclusions. This point of view gives rise to two important observations, both pointing at some flaws of other theories. The first is that arguments should be compared as they are constructed, viz. step-by-step, while the second observation is that a knowledge representation language is needed with a defeasible conditional, since the material implication gives rise to arguments which are not constructed in actual reasoning. Accordingly, a nonmonotonic logic, default logic, is chosen as the formalism underlying the argumentation framework. The general structure of the framework allows for any standard for comparing pairs of arguments; in this study two such standards are investigated, based on specificity and on orderings of the premises.     

A Dialogue between a Lawyer and Computer Scientist: The Evaluation of Knowledge Transformation from Legal Text to Computer-Readable Format

Involvement of a domain expert in domain knowledge modeling, for its use in an automated decision support system, has been a subject of interest in the last few decades. The process of knowledge creation and its transformation for further reasoning requires verification and validation. In this article, we provide details of the first approaches to a process that uses the well-known CQ-based evaluation methodology to identify and evaluate the process of legal-knowledge creation using SBVR-SE and its transformation to OWL 2. Thereby, it increases the legal practitioner’s confidence in using the created and transformed knowledge within a decision support system for deriving inferences for an in-court argumentation.     

The problem of demodulating across argument and literal boundaries

This article is the twenty-fourth of a series of articles discussing various open research problems in automated reasoning. The problem proposed for research asks one to find an appropriate theory for modulating across argument and across literal boundaries. Because demodulation has proved so useful—is most cases, even crucial—to automated reasoning, extending this concept to permit canonicalization to be applied at the predicate and at the clause and subclause levels merits exploration. For evaluating a proposed solution to this research problem, we suggest problems from mathematics, logic, program verification, database inquiry, and the world of puzzles.This work was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

A new use of an automated reasoning assistant: Open questions in equivalential calculus and the study of infinite domains

The field of automated reasoning is an outgrowth of the field of automated theorem proving. The difference in the two fields is not so much in the procedures on which they rest, but rather in the way the corresponding programs are used. Here we present a comprehensive treatment of the use of an automated reasoning program to answer certain previously open questions from equivalential calculus. The questions are answered with a uniform method that employs schemata to study the infinite domain of theorems deducible from certain formulas. We include sufficient detail both to permit the work to be duplicated and to enable one to consider other applications of the techniques. Perhaps more important than either the results or the methodology is the demonstration of how an automated reasoning program can be used as an assistant and a colleague. Precise evidence is given of the nature of this assistance.