"Modeling Legal Argument "provides a comprehensive treatment of case-based reasoning and a detailed description of a computer program called Hypo, that models the way attorneys argue with cases, real and hypothetical. The program offers significant advantages over "keyword" case retrieval systems in the legal field and demonstrates how to design expert systems that assist the user by presenting reasonable alternative answers on all sides of an issue and by citing case examples to explain their advice.Hypo analyzes problem situations dealing with trade secrets disputes, retrieves relevant legal cases from its database and fashions them into reasonable legal arguments about who should win. The arguments demonstrate the program's ability to reason symbolically with past cases, to draw factual analogies between cases, to cite them in arguments, to distinguish them, and to pose counter-examples and hypotheticals based on past cases."Modeling Legal Argument "discusses the law as a paradigm of case-based argument, introduces Hypo and its adversarial reasoning process, provides an overview of the Hypo program, and gives extended examples of the model's reasoning capabilities. It describes the case knowledge base, a dimensional index, basic mechanisms of case-based reasoning, and offers a theory of case-based argument in Hypo. Ashley evaluates Hypo's performance and takes up adversarial case-based reasoning beyond the law and extensions of the Hypo model.Kevin D. Ashley is a Research Scientist at the Learning Research an Development Center and Assistant Professor of Law at the University of Pittsburgh. "Modeling Legal Argument is "included in the Artificial Intelligence and Legal Reasoning series, edited by L. Thorne McCarty and Edwina L. Rissland.

First published in 1998. This five-volume series contains some of this century's most influential or thought provoking articles on the subject of legal argument that have appeared in Anglo-American philosophy journals and law reviews. This volume offers a collection of essays by philosophers and legal scholars on economics, artificial intelligence and the physical sciences.

This book is a revised and extended version of my PhD Thesis 'Logical Tools for Modelling Legal Argument', which I defended on 14 January 1993 at the Free University Amsterdam. The first five chapters of the thesis have remained almost completely unchanged but the other chapters have undergone considerable revision and expansion. Most importantly, I have replaced the formal argument-based system of the old Chapters 6, 7 and 8 with a revised and extended system, whieh I have developed during the last three years in collaboration with Giovanni Sartor. Apart from some technical improvements, the main additions to the old system are the enriehment of its language with a nonprovability operator, and the ability to formalise reasoning about preference criteria. Moreover, the new system has a very intuitive dialectieal form, as opposed to the rather unintuitive fixed-point appearance of the old system. Another important revision is the split of the old Chapter 9 into two new chapters. The old Section 9. 1 on related research has been updated and expanded into a whole chapter, while the rest of the old chapter is now in revised form in Chapter 10. This chapter also contains two new contributions, a detailed discussion of Gordon's Pleadings Game, and a general description of a multi-Iayered overall view on the structure of argu mentation, comprising a logieal, dialectical, procedural and strategie layer. Finally, in the revised conclusion I have paid more attention to the relevance of my investigations for legal philosophy and argumentation theory.

The investigation of computational models of argument is a rich and fascinating interdisciplinary research field with two ultimate aims: the theoretical goal of understanding argumentation as a cognitive phenomenon by modeling it in computer programs, and the practical goal of supporting the development of computer-based systems able to engage in argumentation-related activities with human users or among themselves. The biennial International Conferences on Computational Models of Argument (COMMA) provide a dedicated forum for the presentation and discussion of the latest advancements in the field, and cover both basic research and innovative applications. This book presents the proceedings of COMMA 2020. Due to the Covid-19 pandemic, COMMA 2020 was held as an online event on the originally scheduled dates of 8 -11 September 2020, organised by the University of Perugia, Italy. The book includes 28 full papers and 13 short papers selected from a total of 78 submissions, the abstracts of 3 invited talks and 13 demonstration abstracts. The interdisciplinary nature of the field is reflected, and contributions cover both theory and practice. Theoretical contributions include new formal models, the study of formal or computational properties of models, designs for implemented systems and experimental research. Practical papers include applications to medicine, law and criminal investigation, chatbots and online product reviews. The argument-mining trend from previous COMMA’s is continued, while an emerging trend this year is the use of argumentation for explainable AI. The book provided an overview of the latest work on computational models of argument, and will be of interest to all those working in the field.

A new proposal for integrating the employment of formal and empirical methods in the study of human reasoning. In Human Reasoning and Cognitive Science, Keith Stenning and Michiel van Lambalgen—a cognitive scientist and a logician—argue for the indispensability of modern mathematical logic to the study of human reasoning. Logic and cognition were once closely connected, they write, but were “divorced” in the past century; the psychology of deduction went from being central to the cognitive revolution to being the subject of widespread skepticism about whether human reasoning really happens outside the academy. Stenning and van Lambalgen argue that logic and reasoning have been separated because of a series of unwarranted assumptions about logic. Stenning and van Lambalgen contend that psychology cannot ignore processes of interpretation in which people, wittingly or unwittingly, frame problems for subsequent reasoning. The authors employ a neurally implementable defeasible logic for modeling part of this framing process, and show how it can be used to guide the design of experiments and interpret results.

Few areas of human expertise are so well understood that they can be completely reduced to general principles. Similarly, there are few domains in which experience is so extensive that every new problem precisely matches a previous problem whose solution is known. When neither rules nor examples are individually sufficient, problem-solving expertise depends on integrating both. This book presents a computational framework for the integration of rules and cases for analytic tasks typified by legal analysis. The book uses the framework for integrating cases and rules as a basis for a new model of legal precedents. This model explains how the theory under which a case is decided controls the case's precedential effect. The framework for integrating rules and cases is implemented in GREBE, a system for legal analysis. The book presents techniques for representing, indexing, and comparing complex cases and for converting justification structures based on rules and case into natural-language text. This book will interest researchers in artificial intelligence, particularly those involved in case-based reasoning, artificial intelligence and law, and formal models of argumentation, and to scholars in legal philosophy, jurisprudence, and analogical reasoning.

Systematically presented to enhance the feasibility of fuzzy models, this book introduces the novel concept of a fuzzy network whose nodes are rule bases and their interconnections are interactions between rule bases in the form of outputs fed as inputs.