Abstract argumentation frameworks (AFs) provide the basis for various reasoning problems in the area of Artificial Intelligence. Efficient evaluation of AFs has thus been identified as an important research challenge. So far, implemented systems for evaluating AFs have either followed a straight-forward reduction-based approach or been limited to certain tractable classes of AFs. In this work, we present a generic approach for reasoning over AFs, based on the novel concept of complexity-sensitivity. Establishing the theoretical foundations of this approach, we derive several new complexity results for preferred, semi-stable and stage semantics which complement the current complexity landscape for abstract argumentation, providing further understanding on the sources of intractability of AF reasoning problems. The introduced generic framework exploits decision procedures for problems of lower complexity whenever possible. This allows, in particular, instantiations of the generic framework via harnessing in an iterative way current sophisticated Boolean satisfiability (SAT) solver technology for solving the considered AF reasoning problems. First experimental results show that the SAT-based instantiation of our novel approach outperforms existing systems.

Computational models of argument could play a valuable role in persuasion technologies for behaviour change (e.g. persuading a user to eat a more healthy diet, or to drink less, or to take more exercise, or to study more conscientiously, etc). For this, the system (the persuader) could present arguments to convince the user (the persuadee). In this paper, we consider asymmetric dialogues where only the system presents arguments, and the system maintains a model of the user to determine the best choice of arguments to present (including counterarguments to key arguments believed to be held by the user). The focus of the paper is on the user model, including how we update it as the dialogue progresses, and how we use it to make optimal choices for dialogue moves.

Abstract dialectical frameworks (ADFs) are a powerful generalization of Dung’s abstract argumentation frameworks. ADFs allow to model argumentation scenarios such that ADF semantics then provide interpretations of the scenarios. Among the considerable number of ADF semantics, the naive-based ones are built upon the fundamental concept of conflict-freeness. Intuitively, a three-valued interpretation of an ADF’s statements is conflict-free iff all true statements can possibly be accepted, and all false statements cannot possibly be accepted. In this paper, we perform an exhaustive analysis of the computational complexity of naive-based semantics. The results are quite interesting, for some of them involve little-known classes of the so-called Boolean hierarchy (another hierarchy in between classes of the polynomial hierarchy). Furthermore in credulous and sceptical entailment, the complexity can be different depending on whether we check for truth or falsity of a specific statement.

Argumentation is an inherently dynamic process. Given that argumentation can be viewed as a process as well Consequently, recent years have witnessed tremendous as a product, recent years have seen an increasing number of research efforts towards an understanding of studies on different problems in the dynamics of argumentation how the seminal AGM theory of belief change can frameworks [Baumann, 2012; Bisquert et al., 2011; 2013; be applied to argumentation, in particular for Dung's Boella et al., 2009; Booth et al., 2013; Cayrol et al., 2010; abstract argumentation frameworks (AFs). However, Doutre et al., 2014; Kontarinis et al., 2013; Krümpelmann et none of the attempts has yet succeeded in handling al., 2012; Nouioua and Würbel, 2014; Sakama, 2014]. The the natural situation where the revision of an AF is problem we tackle here is how to revise an AF when some new guaranteed to be representable by an AF as well.

We study the problem of aggregation of Dung's abstract argumentation frameworks. Some operators for this aggregation have been proposed, as well as some rationality properties for this process. In this work we study the existing operators and new ones that we propose in light of the proposed properties, highlighting the fact that existing operators do not satisfy a lot of these properties. The conclusions are that on one hand none of the existing operators seem fully satisfactory, but on the other hand some of the properties proposed so far seem also too demanding.

Change in abstract argumentation frameworks (AFs) is a very active topic. Especially, the problem of enforcing a set E of arguments, i.e., ensuring that E is an extension (or a subset of an extension) of a given AF F, has received a particular attention in the recent years. In this paper, we define a new family of enforcement operators, for which enforcement can be achieved by adding new arguments (and attacks) to F (as in previous approaches to enforcement), but also by questioning some attacks (and non-attacks) of F. This family includes previous enforcement operators, but also new ones for which the success of the enforcement operation is guaranteed. We show how the enforcement problem for the operators of the family can be modeled as a pseudo-Boolean optimization problem. Intensive experiments show that the method is practical and that it scales up well.

In this paper we combine two of the most important areas of knowledge representation, namely belief revision and (abstract) argumentation. More precisely, we show how AGM-style expansion and revision operators can be defined for Dung's abstract argumentation frameworks (AFs). Our approach is based on a reformulation of the original AGM postulates for revision in terms of monotonic consequence relations for AFs. The latter are defined via a new family of logics, called Dung logics, which satisfy the important property that ordinary equivalence in these logics coincides with strong equivalence for the respective argumentation semantics. Based on these logics we define expansion as usual via intersection of models. We show the existence of such operators. This is far from trivial and requires to study realizability in the context of Dung logics. We then study revision operators. We show why standard approaches based on a distance measure on models do not work for AFs and present an operator satisfying all postulates for a specific Dung logic.

The adoption of a generic contrariness notion in ASPIC+ substantially enhances its expressiveness with respect to other formalisms for structured argumentation. In particular, it opens the way to novel investigation directions, like the use of multivalued logics in the construction of arguments. This paper points out however that in the current version of ASPIC+ a serious technical difficulty related with generic contrariness is present. With the aim of preserving the same level of generality, the paper provides a solution based on a novel notion of closure of the contrariness relation at the level of sets of formulas and an abstract representation of conflicts between sets of arguments. The proposed solution is shown to satisfy the same rationality postulates as ASPIC+ and represents a starting point for further technical and conceptual developments in structured argumentation.

One prominent way to deal with conflicting view-points among agents is to conduct an argumentative debate: by exchanging arguments, agents can seek to persuade each other. In this paper we investigate the problem, for an agent, of optimizing a sequence of moves to be put forward in a debate, against an opponent assumed to behave stochastically, and equipped with an unknown initial belief state. Despite the prohibitive number of states induced by a naive mapping to Markov models, we show that exploiting several features of such interaction settings allows for optimal resolution in practice, in particular: (1) as debates take place in a public space (or common ground), they can readily be modelled as Mixed Observability Markov Decision Processes, (2) as argumentation problems are highly structured, one can design optimization techniques to prune the initial instance. We report on the experimental evaluation of these techniques.

The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework. The theory is based on a generalisation of Dung’s notion of acceptability, making it sensitive to the numbers of attacks and counter-attacks on arguments. Graded generalisations of argumentation semantics are then obtained and studied. The theory is applied by showing how it can arbitrate between competing preferred extensions and how it captures a specific form of accrual in instantiated argumentation.