Computational models of argument

Argumentation is a generic approach for solving different AI problems including defeasible reasoning, decision making, classification, etc. It is based on the justification of claims by arguments.

In this topic, our research can be partitioned into 3 distinct fields.

Evaluation of arguments

A key step of the approach based on argumentation is the evaluation of arguments. This notion of evaluation can be studied following two points of view: we can consider either an objective evaluation of the arguments (objective in the sense that the impact on the “target of the argumentation task” is not taken into account) or a cognitive one.

Considering the objective point of view, since 1993, several evaluation methods, called semantics, were proposed. However, their foundations were largely unexplored. We laid the foundations of semantics; we proposed axioms or principles that should be satisfied by reasonable semantics and investigated four types of argumentation graphs: flat attack graphs, weighted attack graphs, weighted support graphs, weighted bipolar graphs; for each type of graphs, we analyzed and compared existing semantics, and proposed novel ones that satisfy the axioms. Also, we have conducted an analysis (subsumption, incompatibility,..) of principles, adapted or dedicated, to ranking-based semantics. We also proposed new semantics in order to complete the panoramic view of the extensions of the classical framework defined by Dung (argumentation graphs with recursive/high-order attack/support). Beside this core, some other interesting points have been studied: for instance a structural analysis of some semantics using a matrix approach.

Considering the cognitive evaluation of arguments, we have proposed in a formal analysis of the persuasive impact that an argument can produce on a human agent. This analysis allows the definition of a formal model based on the dual process theory proposed by Kahneman. Then this model has been applied to the automatic evaluation of arguments. This work is the first step towards a best understanding of some crucial aspects of the collective decision using argumentation strategies and persuasion process.

Languages and algorithms

It is of course important to define and characterize the evaluation of arguments. Nevertheless, in practice, it remains useless if we are not able to compute them in a realistic time. That leads us to consider both languages and algorithms for evaluation of arguments.

We laid the foundations of a logic of argumentation in which arguments, as well as attacks and supports among arguments are all defined in a unifying formalism. We introduced a series of inference rules relating arguments and showed how the resulting logic captures important features of argumentation that hitherto have not been captured by existing formalisms. We have also proposed a translation of abstract argumentation graphs (flat, bipolar and recursive) into logical formalisms. Another approach was the definition of a logic able to support argumentation and logic programming.

The study of these different logical approaches for argumentation has allowed us to implement some tools and to open the door to algorithms based on efficient SAT solvers for computing argument evaluation (the more efficient the translation and the SAT solver, the larger the argumentation graphs we can evaluate). Note that, in parallel, a study of the links between abstract argumentation and logic is the subject of a CIMI project.

Argument mining  

Argument mining is a highly complex information retrieval task. Given a claim, the aim is to find, in a large variety of texts, propositions that support or attack it, how, with what strength and on the basis of what knowledge and reasoning schemes. In the past years, from linguistic and conceptual evidence, we developed an original approach: knowledge-based argument mining which offers a high analysis accuracy and a good explanation power. This approach enables a better understanding of argumentative processes than, e.g., systems based on learning from annotations.


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