Modeling Legal Argument

"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.

In the study of forms of legal reasoning, logic and argumentation theory long followed separate tracks. `Legal logicians' tended to focus on a deductive reconstruction of justifying a decision, disregarding the dialectical process leading to the chosen justification. Others instead emphasized the adversarial and discretionary nature of legal reasoning, involving reasonable evaluation of alternative choices, and the use of analogical reasoning. Recently, however, developments in Artificial Intelligence and Law have paved the way for overcoming this separation. Logic has widened its scope to defensible argumentation, and informal accounts of analogy and dialectics have inspired the construction of computer programs. Thus the prospect is emerging of an integrated logical and dialectical account of legal argument, adding to the understanding of legal reasoning, and providing a formal basis for computer tools that assist and mediate legal debates while leaving room for human initiative. This book presents contributions to this development. From a logical point of view it covers topics such as evaluating conflicting arguments, weighing reasons, modelling legal disputes as a dialogue game, the role of the burden of proof, the relation between principles, rules, reasons and facts, and the relation between deductive and nondeductive arguments. Written by leading scholars in the field and building on recent developments in logic and Artificial Intelligence, the chapters provide a state-of-the-art account of research on the logical aspects of legal argument.

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.

This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems.

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.

In the study of forms of legal reasoning, logic and argumentation theory long followed separate tracks. `Legal logicians' tended to focus on a deductive reconstruction of justifying a decision, disregarding the dialectical process leading to the chosen justification. Others instead emphasized the adversarial and discretionary nature of legal reasoning, involving reasonable evaluation of alternative choices, and the use of analogical reasoning. Recently, however, developments in Artificial Intelligence and Law have paved the way for overcoming this separation. Logic has widened its scope to defensible argumentation, and informal accounts of analogy and dialectics have inspired the construction of computer programs. Thus the prospect is emerging of an integrated logical and dialectical account of legal argument, adding to the understanding of legal reasoning, and providing a formal basis for computer tools that assist and mediate legal debates while leaving room for human initiative. This book presents contributions to this development. From a logical point of view it covers topics such as evaluating conflicting arguments, weighing reasons, modelling legal disputes as a dialogue game, the role of the burden of proof, the relation between principles, rules, reasons and facts, and the relation between deductive and nondeductive arguments. Written by leading scholars in the field and building on recent developments in logic and Artificial Intelligence, the chapters provide a state-of-the-art account of research on the logical aspects of legal argument.

Demystifying Legal Reasoning defends the proposition that there are no special forms of reasoning peculiar to law. Legal decision makers engage in the same modes of reasoning that all actors use in deciding what to do: open-ended moral reasoning, empirical reasoning, and deduction from authoritative rules. This book addresses common law reasoning when prior judicial decisions determine the law, and interpretation of texts. In both areas, the popular view that legal decision makers practise special forms of reasoning is false.

This handbook addresses legal reasoning and argumentation from a logical, philosophical and legal perspective. The main forms of legal reasoning and argumentation are covered in an exhaustive and critical fashion, and are analysed in connection with more general types (and problems) of reasoning. Accordingly, the subject matter of the handbook divides in three parts. The first one introduces and discusses the basic concepts of practical reasoning. The second one discusses the general structures and procedures of reasoning and argumentation that are relevant to legal discourse. The third one looks at their instantiations and developments of these aspects of argumentation as they are put to work in the law, in different areas and applications of legal reasoning.