Douglas Walton’s work is extremely vast, multifaceted, and interdisciplinary. He developed theoretical proposals that have been used in disciplines that are not traditionally related to philosophy, such as law, education, discourse analysis, artificial intelligence, or medical communication. Through his papers and books, Walton redefined the boundaries not only of argumentation theory, but also logic and philosophy. He was a philosopher in the sense that his interest was developing theoretical models that can help explain reality, and more importantly interact with it. (...) For this reason, he proposed methods that have been used for analyzing different types of dialogical interactions, and modeling procedures for regulating them. (shrink)

We propose a revised definition of “argument scheme” that focuses on describing argumentative performances and normative assessments that occur within an argumentative context, the social context in which the scheme arises. Our premise-and-conclusion structure identifies the typical instantiation of an argument in the argumentative context, and our critical framework describes a set of normative assessments available to participants in the context, what we call _practically normative_ assessments. We distinguish this practical normativity from the _rationally or universally normative_ assessment that might (...) be imposed from outside the argumentative context. Thus, the practical norms represented in an argument scheme may still be subject to rational critique, and the scheme avoids the is/ought fallacy. We ground our theoretical discussion and observations in an empirical study of US district court opinions resolving legal questions about copyright fair use and the lawyers’ briefs that led to them, instantiating our definition of argument scheme in the “argument for classification by precedent.” Our definition addresses some criticisms the argument-scheme construct has received. For example, using our data, we show that a minimally well formed instance of this type of argument does not shift any conventional burden from the proponent of the argument to its skeptics. We also argue that these argument schemes need not be seen as dialogical. (shrink)

Computational models of legal precedent-based reasoning developed in AI and Law are typically based on the simplifying assumption that the background set of precedent cases is consistent. Besides being unrealistic in the legal domain, this assumption is problematic for recent promising applications of these models to the development of explainable AI methods. In this paper I explore a model of legal precedent-based reasoning that, unlike existing models, does not rely on the assumption that the background set of precedent cases is (...) consistent. The model is a generalization of the reason model of precedential constraint. I first show that the model supports an interesting deontic logic, where consistent obligations can be derived from inconsistent case bases. I then provide an explanation of this surprising result by proposing a reformulation of the model in terms of cases that support a new potential decision and cases that conflict with it. Finally, I show that the reformulation of the model allows us to verify that inconsistent case bases do not make verification that a decision is permissible substantially more complex than consistent case bases and to introduce intuitive criteria to compare different permissible decisions. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks.

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely (...) argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments. (shrink)