Showing papers by "Douglas Walton published in 2015"

A Classification System for Argumentation Schemes

Abstract: This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

Argument Evaluation and Evidence

Abstract: This monograph poses a series of key problems of evidential reasoning and argumentation. It then offers solutions achieved by applying recently developed computational models of argumentation made available in artificial intelligence. Each problem is posed in such a way that the solution is easily understood. The book progresses from confronting these problems and offering solutions to them, building a useful general method for evaluating arguments along the way. It provides a hands-on survey explaining to the reader how to use current argumentation methods and concepts that are increasingly being implemented in more precise ways for the application of software tools in computational argumentation systems. It shows how the use of these tools and methods requires a new approach to the concepts of knowledge and explanation suitable for diverse settings, such as issues of public safety and health, debate, legal argumentation, forensic evidence, science education, and the use of expert opinion evidence in personal and public deliberations.

Classifying the Patterns of Natural Arguments

Abstract: The representation and classification of the structure of natural arguments has been one of the most important aspects of Aristotelian and medieval dialectical and rhetorical theories. This traditional approach is represented nowadays in models of argumentation schemes. The purpose of this article is to show how arguments are characterized by a complex combination of two levels of abstraction, namely, semantic relations and types of reasoning, and to provide an effective and comprehensive classification system for this matrix of semantic and quasilogical connections. To this purpose, we propose a dichotomous criterion of classification, transcending both levels of abstraction and representing not what an argument is but how it is understood and interpreted. The schemes are grouped according to an end-means criterion, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it.

Goal-based Reasoning for Argumentation

Abstract: This book provides an argumentation model for means end-reasoning, a distinctive type of reasoning used for problem-solving and decision-making. Means end-reasoning is modeled as goal-directed argumentation from an agent's goals and known circumstances, and from an action selected as a means, to a decision to carry out the action. Goal-based Reasoning for Argumentation provides an argumentation model of this kind of reasoning showing how it is employed in settings of intelligent deliberation where agents try to collectively arrive at a conclusion on what they should do to move forward in a set of circumstances. The book explains how this argumentation model can help build more realistic computational systems of deliberation and decision-making, and shows how such systems can be applied to solve problems posed by goal-based reasoning in numerous fields, from social psychology and sociology, to law, political science, anthropology, cognitive science, artificial intelligence, multi-agent systems, and robotics.

The Basic Slippery Slope Argument

Abstract: Although studies have yielded a detailed taxonomy of types of slippery slope arguments, they have failed to identify a basic argumentation scheme that applies to all. Therefore, there is no way of telling whether a given argument is a slippery slope argument or not. This paper solves the problem by providing a basic argumentation scheme. The scheme is shown to fit a clear and easily comprehensible example of a slippery slope argument that strongly appears to be reasonable, something that has also been lacking.

Formalizing Informal Logic

Abstract: This paper presents a formalization of informal logic using the Carneades Argumentation System (CAS), a formal, computational model of argument that consists of a formal model of argument graphs and audiences. Conflicts between pro and con arguments are resolved using proof standards, such as preponderance of the evidence. CAS also formalizes argumentation schemes. Schemes can be used to check whether a given argument instantiates the types of argument deemed normatively appropriate for the type of dialogue.

Profiles of Dialogue: A Method of Argument Fault Diagnosis and Repair

Abstract: This paper builds the profiles of dialogue tool into a fault diagnosis method that can be applied to problematic examples of argumentation such as those involving informal fallacies. The profiles method works by comparing a descriptive graph with a normative graph. The descriptive graph represents how a dialogue sequence actually went in the example chosen for analysis. The normative graph represents an analysis of how the sequence should ideally proceed, according to the protocols (rules) for this type of dialogue. The descriptive graph is mapped into the normative graph, so that a comparison can be made to diagnose the fault in the sequence displayed in the descriptive graph and repair it.

The Importance and Trickiness of Definition Strategies in Legal and Political Argumentation

Abstract: This paper uses argumentation tools to show by means of analyzing nine cases from law and politics how argument strategies using persuasive definitions and quasi-definitions are powerful rhetorical tools of persuasion. By bringing to lightthe argumentation structure found in these examples, it is shown that definitions and redefinitions can have serious legal and political implications. Persuasive definitions and quasi-definitions are modeled as two distinct strategies for altering the relationship between classification and evaluation of a state of affairs. Persuasive definitions are aimed at modifying the relationship between the definiendum and its referent. In quasi-definitions some characteristics of an entity or event leading to a specific value judgment are selected and made accessible, while other conflicting ones are excluded. Reframing an issue is shown to be related to both strategies.

Classifying the Patterns of Natural Arguments.

Abstract: The representation and classification of the structure of natural arguments has been one of the most important aspects of Aristotelian and medieval dialectical and rhetorical theories. This traditional approach is represented nowadays in models of argumentation schemes. The purpose of this article is to show how arguments are characterized by a complex combination of two levels of abstraction, namely, semantic relations and types of reasoning, and to provide an effective and comprehensive classification system for this matrix of semantic and quasilogical connections. To this purpose, we propose a dichotomous criterion of classification, transcending both levels of abstraction and representing not what an argument is but how it is understood and interpreted. The schemes are grouped according to an end-means criterion, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it.

Profiles of Dialogue: A Method of Argument Fault Diagnosis and Repair

Abstract: Studies of arguments can be heuristically grouped into those that describe how actual arguments are advanced and those that normatively prescribe how arguments should function. The descriptive approaches often seek to identify fallacies or some other problem of logic or communication. Such analyses may examine news reports, Internet sources such as Debatepedia, legal trials, parliamentary debates, or congressional transcripts. Alternatively, abstract normative models of argumentation have been built to formalize structures with dialogue rules (protocols) that stipulate what kinds of moves (speech acts) are permissible and obligatory in sequences of argumentative exchanges. Normative argument analysts are increasingly turning to the field of computer science to encode and graphically represent arguments. There are now developments in artificial intelligence and multiagent systems to model complex exchanges where arguments are both advanced and contested. This paper shows how to apply the profiles of dialogue method to the texts of real arguments with the goal of bringing the descriptive and normative approaches closer together. In the informal logic textbooks, fallacies were traditionally taken to be arguments that are inherently fallacious by virtue of their structure or relations between claims and premises. There has been a gradual revolution in the subject starting with (Hamblin, 1970) that such arguments can sometimes be reasonable. Proving beyond doubt whether or not an argument can properly be diagnosed as defective is the key issue facing argument analysts. Formal dialogue models provide part of the answer by using the context in which the argument occurs in modeling the conditions under which an example of argumentation is not fallacious. These models provide rules to determine whether the dialogue proceeds in the correct order, whether arguers ask necessary preliminary questions, and whether arguers' commitments are properly established. Using the method of profiles of dialogue developed in this paper, an interpretative reconstruction of a naturally occurring argument can be derived using evidence from the text and compared to the requirements of the abstract, formal model. I will show how formal argumentation systems found in artificial intelligence can be used to evaluate arguments and to support the profiles of dialogue method. In the next to last section, a twelve-step procedure is presented explaining how to employ the method as a way to help an argument analyst diagnose, and even repair, faulty arguments such as informal fallacies. The last section shows how this method has both normative and descriptive parts. NORMATIVE AND DESCRIPTIVE APPROACHES Reed, Wells, Rowe, and Devereux (2008, p. 2) identify the distinction between normative and descriptive aspects of argumentation by differentiating prescriptive normative structures defined by abstract procedural rules for formal dialogue systems from representations of actual arguments in actual dialogue histories. It has also been observed that there is quite a gap between formulations of abstract normative argumentation structures in formal models, and actual practices of argumentation in public debates and political controversies (Bruschke, 2004). These latter kinds of studies tend to be more descriptive and empirical in their methods. The technique of argument diagramming is a normative method of informal logic. An argument diagram is composed of two elements (Freeman, 1991): (1) a set of nodes (vertices, points) representing the premises and conclusions in the sequence of argumentation in a given case, and (2) a set of arrows (lines) representing inferences from premises to conclusions. Each arrow basically represents an inference. However, the arrows also represent arguments from premises to conclusions. In applying this technique, the analyst designates the nodes in the diagram as premises or conclusions, based on his or her interpretations of the natural language text of discourse. …

The Art of Finding Arguments

Abstract: Douglas Walton University of Windsor Kienpointner (1997) showed how the ancient status theory and the Aristotelian theory of topics are parts of an art of argument invention that selects premises to be used in a chain of argumentation from a database of premises accepted by the audience a speaker is trying to persuade. He showed how pursuit of this art of finding arguments, although discredited in the Enlightenment, has recently has been taken up again by argumentation theorists. In this paper it is shown that with the recent advent of computational argumentation systems in artificial intelligence, a technology is now available to help an arguer to find arguments that support her claim, and to refute counter-arguments opposing her claim. The origins of attempts to build a systematic method of finding arguments to persuade an audience to come to accept some proposition they were doubtful about or even disagreed with can be found in ancient Greek philosophy and rhetoric. The tradition persisted through the Middle Ages, but was severely criticized by Antoine Arnauld in the 17th century. He claimed that only good knowledge of the subject is needed for finding arguments, and that no special technique of the kind employed by Aristotle and the ancient rhetoricians is required (Kienpointner, 1997, 228). This approach was called by Kienpointner (1997, 230) “encyclopedic”. Nowadays people who accept this view might think that the best way of finding arguments is on the Internet by using resources like Google, Wikipedia and Debatepedia. 1 On this view, all that matters are “the facts”. At the opposite extreme of the encyclopedic approach, creativity techniques such as “brainstorming” impose hardly any restrictions on the finding process: all ideas are welcome and no criticism is allowed (Kienpointner, 1997, 231). This paper shows the way toward finding a middle way to go forward between these two extremes. In this paper it is shown that we are entering a new era in which technologies for helping a user to find arguments to persuade an audience will be made possible by new computational argumentation systems currently being built in artificial intelligence. Sections 1 and 2 present the historical background of the art of finding arguments, based on the survey of (Kienpointner, 1997). In section 3 it is shown how the general framework on which such systems are being built is basically that of knowledge-based systems, such as the early expert systems developed in artificial intelligence. It is shown how such artificial intelligence systems provide a framework in which propositions accepted by an audience can be used as a knowledge base to move a chain of argumentation forward towards the ultimate conclusion to be proved by an arguer. In section 4 a simple example about the issue of whether Wikipedia is reliable is used to illustrate the fundamentals of how such a system of argument invention works to find new argument to strengthen a line of argumentation to support an arguer’s claim. Section 5 provides a slightly more complex example in which it is shown how a given argument put forward by a proponent can be attacked successfully by an opponent who casts around for new argument used for this purpose. Then it is shown how the proponent can use the same technique of argument invention to find new argument to attack the argumentation of his opponent. Section 6 briefly outlines three recent computational argumentation systems that have the capabilities required to build an argument invention system of the kind described in the previous sections. Section 7 presents conclusions suggested by these findings. 1 Debatepedia has a database (http://idebate.org/debatabase) of written debates with facts and examples both for and against on hundreds of issues.

Cumulative Arguments in Artificial Intelligence and Informal Logic

Abstract: We define a new type of argument structure specifically for modeling cumulative arguments and then show how this structure is general enough to simulate linked and convergent arguments. Argumentation schemes are associated with argument weighing functions in this language, where the weight of an argument can depend on the status (labeling) of its premises in an argument graph. Several key examples are used to illustrate the modeling of cumulative arguments, as well as linked and convergent arguments, with this approach. One hypothesis suggested by the analysis of these examples is that cumulative arguments can be treated in the same way as what is called argument accrual in artificial intelligence.

Analogical Reasoning and Semantic Rules of Inference

Abstract: Analogy is represented as a twofold process of abstraction and species-genus inference. This type of analysis can account for essential (i.e. intentional) and accidental similarities. In dialectical analogies, the ones analyzed in the Topics, the abstraction singles out a feature that is part of or related to the meaning of the terms and that is relevant under the respect imposed by the analogical predicate. This process can shed light on the mechanisms underlying reasoning from accidental similarity analysed in the Rhetoric.

A Classification System for Argumentation Schemes

Abstract: This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

Formalizing Informal Logic

Abstract: In this paper we investigate the extent to which formal argumentation models can handle ten basic characteristics of informal logic identified in the informal logic literature. By showing how almost all of these characteristics can be successfully modeled formally, we claim that good progress can be made toward the project of formalizing informal logic.

The Basic Slippery Slope Argument

Abstract: Although studies have yielded a detailed taxonomy of types of slippery slope arguments, they have failed to identify a basic argumentation scheme that applies to all. Therefore, there is no way of telling whether a given argument is a slippery slope argument or not. This paper solves the problem by providing a basic argumentation scheme.

Some Artificial Intelligence Tools for Argument Evaluation: An Introduction

Abstract: Even though tools for identifying and analyzing arguments are now in wide use in the field of argumentation studies, so far there is a paucity of resources for evaluating real arguments, aside from using deductive logic or Bayesian rules that apply to inductive arguments. In this paper it is shown that recent developments in artificial intelligence in the area of computational systems for modeling defeasible argumentation reveal a different approach that is currently making interesting progress. It is shown how these systems provide the general outlines for a system of argument evaluation that can be applied to legal arguments as well as everyday conversational arguments to assist a user to evaluate an argument.