Objections, Rebuttals and Refutations

This paper considers how the terms ‘objection,’ ‘rebuttal,’ ‘attack,’ ‘refutation,’ ‘rebutting defeater’ and ‘undercutting defeater’ (often referred to as rebutters versus undercutters) are used in writings on argumentation and artificial intelligence. The central focus is on the term ‘rebuttal.’ A provisional classification system is proposed that provides a normative structure within which the terms can be clarified, distinguished from each other, and more precisely defined

Extensión de los sistemas argumentativos basados en reglas con elementos de argumentación clásica

El objetivo general de esta investigación es mejorar los Sistemas Argumentativos Basados en Reglas (SABR) con elementos presentes en formalismos de argumentación clásica, los cuales aún no han sido considerados en los SABR desarrollados hasta el momento. Una crítica usualmente realizada sobre los SABR es que determinados patrones de razonamiento argumentativo estudiados en otras áreas, y que constituyen importantes aportes a la argumentación, no son considerados por los SABR. Esta investigación tiene como objetivo incorporar dichos aportes a los SABR, lo cual permitirá mejorar tanto los SABR como sus correspondientes implementaciones, representando un avance significativo para los sistemas argumentativos dentro del área de Inteligencia Artificial y Ciencias de la Computación.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Extensión de los sistemas argumentativos basados en reglas con elementos de argumentación clásica

El objetivo general de esta investigación es mejorar los Sistemas Argumentativos Basados en Reglas (SABR) con elementos presentes en formalismos de argumentación clásica, los cuales aún no han sido considerados en los SABR desarrollados hasta el momento. Una crítica usualmente realizada sobre los SABR es que determinados patrones de razonamiento argumentativo estudiados en otras áreas, y que constituyen importantes aportes a la argumentación, no son considerados por los SABR. Esta investigación tiene como objetivo incorporar dichos aportes a los SABR, lo cual permitirá mejorar tanto los SABR como sus correspondientes implementaciones, representando un avance significativo para los sistemas argumentativos dentro del área de Inteligencia Artificial y Ciencias de la Computación.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Logic, Reasoning, Argumentation: Insights from the Wild

This article provides a brief selective overview and discussion of recent research into natural language argumentation that may inform the study of human reasoning on the assumption that an episode of argumentation issues an invitation to accept a corresponding inference. As this research shows, arguers typically seek to establish new consequences based on prior information. And they typically do so vis-à-vis a real or an imagined opponent, or an opponent-position, in ways that remain sensitive to considerations of context, audiences, and goals. Deductively valid inferences remain a limiting case of such reasoning. In view of these insights, it may appear less surprising that allegedly “irrational” behavior can regularly be produced in experimental settings that expose subjects to standardized reasoning tasks

Artificial intelligence as law:Presidential address to the seventeenth international conference on artificial intelligence and law

Information technology is so ubiquitous and AI's progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever

The need of diagrams based on Toulmin schema application: an aeronautical case study

In this article, Justification Diagrams are introduced for structuring evidence to support conclusions that are reached from results of simulation studies. An industrial application is used to illustrate the use of the Justification Diagrams. Adapted from the Toulmin schema, the aim of Justification Diagram is to define a comprehensive, auditable and shareable notation to explain the results, the input data, the assumptions made and the techniques applied, to construct a cogent conclusion. Further, the Justification Diagrams provide a visual representation of the argument that aims to corroborate the specified claims, or conclusions. A large part of this work is based on the application of the Justification Diagrams in the context of the European project, TOICA. The Justification Diagrams were used to structure all justifications that would be needed to convince an authority that a simulation process, and the associated results, upheld a particular conclusion. These diagrams are built concurrently in a product development process that accompanies the various stages of Verification and Validation (V&V) and where, for each design stage of V&V, argumentation is constructed by aggregating evidence and documents produced at this design stage