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.

Using the Controversy over Human Race to Introduce Students to the Identification and the Evaluation of Arguments

“…This is a relevant outcome once it is recognized that students cultivate their critical thinking skills when they are involved in argument evaluation practices (Osborne, 2012 ). Moreover, this result expands on the scope of some notable work carried out previously that has examined the complexity of argument evaluation (Botting, 2016 ; Jørgensen, 2007 ; Selinger, 2014 ; Walton, 2015 , 2016 ).…”

“…Argument assessment is a complex activity because it demands analyzing whether or not the articulation of a claim with a piece of the evidence is solid, strong, rational, and reasonable within an argumentation process. Relevance of intention (Jørgensen, 2007 ), formal representation (Selinger, 2014 ), artificial intelligence (Walton, 2015 , 2016 ), and logical evaluation (Botting, 2016 ) are some of the perspectives from which such complexity has been discussed.…”

How Should AI Interpret Rules? A Defense of Minimally Defeasible Interpretive Argumentation

“…Abstract argumentation frameworks treat arguments as core entities, focusing on the relationships between arguments rather than their internal structures, and define ways to calculate argument acceptability on the basis of their relationships to each other [14]. Since Dung's seminal work on argument acceptability semantics, a significant number of extensions have been created, and studied, and implemented, thus offering many options for synthesizing a network of interpretive arguments and counterarguments (for overviews, see [3,37,67]).…”

Schemes, Critical Questions, and Complete Argument Evaluation

“…The two basic features of our account thus are an inference rule and exceptions to it. Argument schemes, after all, "can be transformed into instances of logical inference rules by adding the connection between premises and conclusion as a conditional premise" (Prakken 2005, 307; see Walton 1996Walton , 2016. Treating schemes as logical constructs therefore entails that "a procedure for evaluating arguments primarily takes the form of a [non-monotonic] logic" (Prakken 2010, 1).…”

“…For example, a normative application of fallacy theory assumes that "many [possibly most] of the fallacies are failed instances of good argument schemes or forms" (Tindale 2007, xiv), although such instances as "[a] 'Straw Man' argument would seem to be always incorrect and have no redeemable instances" (ibid., 12). Argument schemes also are indispensable in computational versions of acceptance-based approaches like Walton (2016), who-viewing schemes and CQs as basic (ibid., 339)-finds them compatible with probabilistic approaches like Verheij's (2014). Hahn andHornikx (2016, 1833) even argue "that the most fruitful approach to developing normative models of argument quality […] combines the argumentation scheme approach with [the Pascalian probability approach known as] Bayesian argumentation."…”