Argumentation is all around us. Letters to the Editor often make points of cons- tency, and “Why” is one of the most frequent questions in language, asking for r- sons behind behaviour. And argumentation is more than ‘reasoning’ in the recesses of single minds, since it crucially involves interaction. It cements the coordinated social behaviour that has allowed us, in small bands of not particularly physically impressive primates, to dominate the planet, from the mammoth hunt all the way up to organized science. This volume puts argumentation on the map in the eld of Arti cial Intelligence. This theme has been coming for a while, and some famous pioneers are chapter authors, but we can now see a broader systematic area emerging in the sum of topics and results. As a logician, I nd this intriguing, since I see AI as ‘logic continued by other means’, reminding us of broader views of what my discipline is about. Logic arose originally out of re ection on many-agent practices of disputation, in Greek Ant- uity, but also in India and China. And logicians like me would like to return to this broader agenda of rational agency and intelligent interaction. Of course, Aristotle also gave us a formal systems methodology that deeply in uenced the eld, and eventually connected up happily with mathematical proof and foundations.

Use of argumentation methods applied to legal reasoning is a relatively new field of study. The book provides a survey of the leading problems, and outlines how future research using argumentation-based methods show great promise of leading to useful solutions. The problems studied include not only these of argument evaluation and argument invention, but also analysis of specific kinds of evidence commonly used in law, like witness testimony, circumstantial evidence, forensic evidence and character evidence. New tools for analyzing these kinds of evidence are introduced.

Recent work in artificial intelligence has increasingly turned to argumentation as a rich, interdisciplinary area of research that can provide new methods related to evidence and reasoning in the area of law. Douglas Walton provides an introduction to basic concepts, tools and methods in argumentation theory and artificial intelligence as applied to the analysis and evaluation of witness testimony. He shows how witness testimony is by its nature inherently fallible and sometimes subject to disastrous failures. At the same time such testimony can provide evidence that is not only necessary but inherently reasonable for logically guiding legal experts to accept or reject a claim. Walton shows how to overcome the traditional disdain for witness testimony as a type of evidence shown by logical positivists, and the views of trial sceptics who doubt that trial rules deal with witness testimony in a way that yields a rational decision-making process.

In the late 1990s, AI witnessed an increasing use of the term 'argumentation' within its bounds: in natural language processing, in user interface design, in logic programming and nonmonotonic reasoning, in Al's interface with the legal community, and in the newly emerging field of multi-agent systems. It seemed to me that many of these uses of argumentation were inspired by (of ten inspired) guesswork, and that a great majority of the AI community were unaware that there was a maturing, rich field of research in Argumentation Theory (and Critical Thinking and Informal Logic) that had been steadily re building a scholarly approach to the area over the previous twenty years or so. Argumentation Theory, on its side; was developing theories and approaches that many in the field felt could have a role more widely in research and soci ety, but were for the most part unaware that AI was one of the best candidates for such application.

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.

Background and techniques for formalizing deductive argumentation in a logic-based framework for artificial intelligence.

This book provides a systematic analysis of many common argumentation schemes and a compendium of 96 schemes. The study of these schemes, or forms of argument that capture stereotypical patterns of human reasoning, is at the core of argumentation research. Surveying all aspects of argumentation schemes from the ground up, the book takes the reader from the elementary exposition in the first chapter to the latest state of the art in the research efforts to formalize and classify the schemes, outlined in the last chapter. It provides a systematic and comprehensive account, with notation suitable for computational applications that increasingly make use of argumentation schemes.