We propose the combination of two known extensions of Dung's abstract Argumentation Frameworks (AFs): Incomplete Argumentation Frameworks (IAFs), where the existence of arguments and attacks may be uncertain, and Higher-Order Argumentation Frameworks (HOAFs), where attacks can be directed not only towards arguments but also towards attacks. As a result, we obtain Incomplete Higher-Order Argumentation Frameworks (IHOAFs), where attacks on attacks can also be uncertain. We take some important initial steps in the formal understanding of this novel framework by providing: (i) the definition of a conservative notion of completion, the hypothetical removal of uncertainty used to reason about argument acceptability in other formalisms for incomplete argumentation; (ii) complexity results for argument acceptability problems; (iii) an analysis of the previous two points in a well-behaved variant of dynamic logic.

Automated negotiation aims at finding agreements between agents with conflicting goals. Existing utility-based approaches guarantee agents satisfaction with negotiation outcomes, especially in multi-issue negotiations where concession mechanisms lead to win-win results. However, they lack explainability and do not consider agents' beliefs. On the other hand, argument-based approaches provide reasons for accepting or rejecting offers but do not include utility modeling for offers or enable concession mechanisms in multi-issue settings. We propose a novel hybrid approach combining both types of approaches. The utility-based component enables agents to make concessions on complex negotiation objects to achieve win-win outcomes, while the argumentation component ensures that accepted offers align with the agents' personal argumentation theories. These theories represent their beliefs, encoding various profiles, ethical considerations, social norms, or legal principles.

Recent advancements in algorithms for abstract argumentation make it possible now to solve reasoning problems even with argumentation frameworks of large size, as demonstrated by the results of the various editions of the International Competition on Computational Models of Argumentation (ICCMA). However, the solvers participating to the competition may be hard to use for non-expert programmers, especially if they need to incorporate these algorithms in their own code instead of simply using the command-line interface. Moreover, some ICCMA solvers focus on the ICCMA tracks, and do not implement algorithms for other problems. In this paper we describe pygarg, a Python implementation of the SAT-based approach used in the argumentation solver CoQuiAAS. Contrary to CoQuiAAS and most of the participants to the various editions of ICCMA, pygarg incorporates all problems that have been considered in the main track of any edition of ICCMA. We show how to easily use pygarg via a command-line interface inspired by ICCMA competitions, and then how it can be used in other Python scripts as a third-party library.

In the realm of multi-agent systems, argumentative dialogues for persuasion and negotiation involve autonomous agents exchanging arguments, necessitating continual re-evaluation of argument acceptability. This study introduces a novel approach using modern SAT solving techniques to dynamically reassess the acceptability status of arguments, aligning with various classical semantics. Our method uses the assumption mechanism in SAT solvers, distinguished by minimal assumptions, ensuring practicality.

The growing interest in generalizations of Dung's abstract argumentation frameworks has recently led to the simultaneous and independent discovery of a combination of two of these generalizations: Bipolar Argumentation Frameworks (BAFs), where a relation representing supports between arguments is added, and Incomplete Argumentation Frameworks (IAFs), where the existence of arguments and attacks may be uncertain, resulting in the so-called Incomplete Bipolar Abstract Argumentation Frameworks (IBAFs). This paper digs deeper into such a combination by: (i) providing a thoughtful analysis of the existing notions of completion (the hypothetical removal of uncertainty used in IBAFs to reason about argument acceptability); (ii) proposing, motivating and studying new notions of completion; (iii) throwing new complexity results on argument acceptability problems associated with IBAFs; (iv) encoding these reasoning problems into a lightweight version of dynamic logic.

Incomplete Argumentation Frameworks (IAFs) enrich classical abstract argumentation with arguments and attacks whose actual existence is questionable. The usual reasoning approaches rely on the notion of completion, i.e. standard AFs representing “possible worlds” compatible with the uncertain information encoded in the IAF. Recently, extension-based semantics for IAFs that do not rely on the notion of completion have been defined, using instead new versions of conflict-freeness and defense that take into account the (certain or uncertain) nature of arguments and attacks. In this paper, we give new insights on both the “completion-based” and the “direct” reasoning approaches. First, we adapt the well-known grounded semantics to this framework in two different versions that do not rely on completions. After determining that our new semantics are polynomially computable, we provide a principle-based analysis of these semantics, as well as the “direct” semantics previously defined in the literature, namely the complete, preferred and stable semantics. Finally, we also provide new results regarding the satisfaction of principles by the classical “completion-based” semantics.

In a recently published book, the French writer and comedian François Rollin has discussed various aspects of the notion of stupidity, including artificial stupidity, the stupid counterpart of artificial intelligence. His claim is that a system of artificial stupidity is a system that provides wrong answers to any task it should solve, leading to absurd solutions in most cases. We believe that this claim is (at least partially) false and that designing artificial stupidity is not as trivial as it seems. In this article, we discuss why and how one could design a system of artificial stupidity. We believe that such a reflection on (artificial) stupidity can bring about some interesting insights about (artificial) intelligence.

Various approaches have been proposed for providing efficient computational approaches for abstract argumentation. Among them, neural networks have permitted to solve various decision problems, notably related to arguments (credulous or skeptical) acceptability. In this work, we push further this study in various ways. First, relying on the state-of-the-art approach AFGCN, we show how we can improve the performances of the Graph Convolutional Networks (GCNs) regarding both runtime and accuracy. Then, we show that it is possible to improve even more the efficiency of the approach by modifying the architecture of the network, using Graph Attention Networks (GATs) instead.

Incomplete Argumentation Frameworks (IAFs) have been defined to incorporate some qualitative uncertainty in abstract argumentation: information such as ‘I am not sure whether this argument exists’ or ‘I am not sure whether this argument attacks that one’ can be expressed. Reasoning with IAFs is classically based on a set of completions, i.e. standard argumentation frameworks (AFs) that represent the possible worlds encoded in the IAF. The number of these completions may be exponential with respect to the number of arguments in the IAF. This leads, in some cases, to an increase of the complexity of reasoning, compared to the complexity of standard AFs. In this paper, we follow an approach that was initiated for Partial Argumentation Frameworks (PAFs) (a subclass of IAFs), which consists in defining new forms of conflict-freeness and defense, the properties that underly the definition of Dung’s semantics for AFs. We generalize these semantics from PAFs to IAFs. We show that, among three possible types of admissibility, only two of them satisfy some desirable properties. We use them to define two new families of extension-based semantics. We study the properties of these semantics, and in particular, we show that their complexity remains the same as in the case of Dung’s AFs. Finally, we propose a logical encoding of these semantics, and we show experimentally that this encoding can be used efficiently to reason with IAFs, thanks to the power of modern SAT solvers.

Incomplete Argumentation Frameworks (IAFs) enrich classical abstract argumentation with arguments and attacks whose actual existence is questionable. The usual reasoning approaches rely on the notion of completion, i.e. standard AFs representing “possible worlds” compatible with the uncertain information encoded in the IAF. Recently, extension-based semantics for IAFs that do not rely on the notion of completion have been defined, using instead new versions of conflict-freeness and defense that take into account the (certain or uncertain) nature of arguments and attacks. In this paper, we give new insights on this reasoning approach, by adapting the well-known grounded semantics to this framework in two different versions. After determining the computational complexity of our new semantics, we provide a principle-based analysis of these semantics, as well as the ones previously defined in the literature, namely the complete, preferred and stable semantics.

Similarity in formal argumentation has received some attention recently, since one can argue that, in some context, using similar arguments to reach a conclusion is not the same as using dissimilar ones. While existing work consider arguments built from propositional logic, in this work we adapt the notion of similarity measures to arguments built from Order-Sorted First Order Logic, an extension of First Order Logic which allows to represent complex information, considering the type of the data. We study and evaluate our approach with respect to an adaptation of axioms from the literature. This paves the way to new reasoning modes

Graph generators are a powerful tool to provide benchmarks for various subfields of KR (e.g. abstract argumentation, description logics, etc.) as well as other domains of AI (e.g. resources allocation, gossip problem, etc.). In this paper, we describe a new approach for generating graphs based on the idea of communities, i.e. parts of the graph which are densely connected, but with fewer connections between different communities. We discuss the design of an application named crusti_g2io implementing this idea, and then focus on a use case related to abstract argumentation. We show how crusti_g2io can be used to generate structured hard argumentation instances which are challenging for the fourth International Competition on Computational Models of Argumentation (ICCMA’21) solvers.

We study the notion of realization of extensions in abstract argumentation. It consists in reversing the usual reasoning process: instead of computing the extensions of an argumentation framework, we want to determine whether a given set of extensions corresponds to some (set of) argumentation framework(s) (AFs); and more importantly we want to identify such an AF (or set of AFs) that realizes the set of extensions. While deep theoretical studies have been concerned with realizability of extensions sets, there are few computational approaches for solving this problem. In this paper, we generalize the concept of realizability by introducing two parameters: the number k of auxiliary arguments ( i.e. those that do not appear in any extension), and the number m of AFs in the result. We define a translation of k - m -realizability into Quantified Boolean Formulas (QBFs) solving. We also show that our method allows to guarantee that the result of the realization is as close as possible to some input AF. Our method can be applied in the context of AF revision operators, where revised extensions must be mapped to a set of AFs while ensuring some notion of proximity with the initial AF.

Efficient computation of hard reasoning tasks is a key issue in abstract argumentation. One recent approach consists in defining approximate algorithms, i.e. methods that provide an answer that may not always be correct, but outperforms the exact algorithms regarding the computation runtime. One such approach proposes to use the grounded semantics, which is polynomially computable, as a starting point for determining whether arguments are (credulously or skeptically) accepted with respect to various semantics. In this paper, we push further this idea by defining various approaches to evaluate the acceptability of arguments which are not in the grounded extension, neither attacked by it. We have implemented our approaches, and we describe the result of their empirical evaluation.