Selinger provides a new take on what is being referred to in the computational literature as ‘structured argumentation’. In this commentary the differences and similarities with existing work are highlighted as a way of demonstrating how philosophical and computational approaches to argumentation are increasingly coming together and complementing one another.

The aim of this paper is to propose foundations for a formal model of representation and numerical evaluation of a possibly broad class of arguments, including those that occur in natural discourse. Since one of the most characteristic features of everyday argumentation is the occurrence of convergent reasoning, special attention should be paid to the operation ⊕, which allows us to calculate the logical force of convergent arguments with an accuracy not offered by other approaches.

This paper studies a fundamental mechanism by which conflicts between arguments are drawn from sentiments regarding acceptability of the arguments. Given sets of arguments, an inverse abstract argumentation problem seeks attack relations between arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in each of the attack relations. It is an inverse problem of the traditional problem we refer to as the forward abstract argumentation problem. Given an attack relation, the forward abstract argumentation (...) problem seeks sets of arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in the attack relation. We give a probabilistic model of argumentation-theoretic inference. It is a generative model formalising the process by which acceptability semantics interprets acceptability of arguments in a given attack relation. We show that it gives a broad view of solutions to the forward and inverse abstract argumentation problems. Specifically, solutions to the inverse and forward abstract argumentation problems are shown to be equivalent to a maximum likelihood estimate and maximum likelihood prediction, respectively, which are both available with the generative model. In addition, they are shown to be special cases of the posterior distribution and the evidence, respectively, which are both obtained by probabilistic inference on the generative model. We report an experiment result and application example of the generative model in the inverse problems. (shrink)