Abstract argumentation frameworks (AFs) are one of the central formalisms in AI; equipped with a wide range of semantics, they have proven useful in several application domains. We contribute to the systematic analysis of semantics for AFs by connecting two recent lines of research -- the work on input/output frameworks and the study of the expressiveness of semantics. We do so by considering the following question: given a function describing an input/output behaviour by mapping extensions (resp. labellings) to sets of extensions (resp. labellings), is there an AF with designated input and output arguments realizing this function under a given semantics? For the major semantics we give exact characterizations of the functions which are realizable in this manner.

Formalizing dynamics of argumentation has received increasing attention over the last years. While AGM-like representation results for revision of argumentation frameworks (AFs) are now available, similar results for the problem of merging are still missing. In this paper, we close this gap and adapt model-based propositional belief merging to define extension-based merging operators for AFs. We state an axiomatic and a constructive characterization of merging operators through a family of rationality postulates and a representation theorem. Then we exhibit merging operators which satisfy the postulates. In contrast to the case of revision, we observe that obtaining a single framework as result of merging turns out to be a more subtle issue. Finally, we establish links between our new results and previous approaches to merging of AFs, which mainly relied on axioms from Social Choice Theory, but lacked AGM-like representation theorems.

A central question in knowledge representation is the following: given some knowledge representation formalism, is it possible, and if so how, to simplify parts of a knowledge base without affecting its meaning, even in the light of additional information? The term strong equivalence was coined in the literature, i.e. strongly equivalent knowledge bases can be locally replaced by each other in a bigger theory without changing the semantics of the latter. In contrast to classical (monotone) logics where standard and strong equivalence coincide, it is possible to find ordinary but not strongly equivalent objects for any nonmonotonic formalism available in the literature. This paper addresses these questions in the context of abstract argumentation theory. Much effort has been spent to characterize several argumentation tailored equivalence notions w.r.t. extension-based semantics. In recent times labelling-based semantics have received increasing attention, for example in connection with algorithms computing extensions, proof procedures, dialogue games, dynamics in argumentation as well as belief revision in general. Of course, equivalence notions allowing for replacements are of high interest for the mentioned topics. In this paper we provide kernel-based characterization theorems for semantics based on complete labellings as well as admissible labellings w.r.t. eight different equivalence notions including the aforementioned most prominent one, namely strong equivalence.

In almost all existing semantics in argumentation, a strong attack has a lethal effect on its target that a set of several weak attacks may not have. This paper investigates the case where several weak attacks may compensate one strong attack. It defines a broad class of ranking semantics, called alpha-OBBS, which satisfy compensation. alpha-OBBS assign a burden number to each argument and order the arguments with respect to those numbers. We study formal properties of alpha-OBBS, implement an algorithm that calculates the ranking, and perform experiments that show that the approach computes the ranking very quickly. Moreover, an approximation of the ranking can be provided at any time.

We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. In doing so MARFs provide a principled technique for the merger of probabilistic graphical models and non-monotonic reasoning, supporting human reasoning in ``messy’’ domains where the knowledge about conflicts should be applied. Our implementation takes the form of a graphical tool which supports users in interpreting complex information. We have evaluated our implementation in the domain of intelligence analysis, where analysts must reason and determine likelihoods of events using information obtained from conflicting sources.

The principle of counter-transitivity plays a vital role in argumentation. It states that an argument is strong when its attackers are weak, and is weak when its attackers are strong. In this work, we develop a formal theory about the argument ranking semantics based on this principle. Three approaches, quantity-based, quality-based and the unity of them, are defined to implement the principle. Then, we show an iterative refinement algorithm for capturing the ranking on arguments based on the recursive nature of the principle.

A key acceptability criterion for artificial agents will be the possible moral implications of their actions. In particular, intelligent persuasive systems (systems designed to influence humans via communication) constitute a highly sensitive topic because of their intrinsically social nature. Still, ethical studies in this area are rare and tend to focus on the output of the required action; instead, this work focuses on the acceptability of persuasive acts themselves.Building systems able to persuade while being ethically acceptable requires that they be capable of intervening flexibly and of taking decisions about which specific persuasive strategy to use. We show how, exploiting a behavioral approach, based on human assessment of moral dilemmas, we obtain results that will lead to more ethically appropriate systems. Experiments we have conducted address the type of persuader, the strategies adopted and the circumstances. Dimensions surfaced that can characterize the interpersonal differences concerning moral acceptability of machine performed persuasion, usable for strategy adaptation. We also show that the prevailing preconceived negative attitude toward persuasion by a machine is not predictive of actual moral acceptability judgement when subjects are confronted with specific cases.

Understanding the dynamics of argumentation frameworks (AFs) is important in the study of argumentation in AI. In this work, we focus on the so-called extension enforcement problem in abstract argumentation. We provide a nearly complete computational complexity map of fixed-argument extension enforcement under various major AF semantics, with results ranging from polynomial-time algorithms to completeness for the second-level of the polynomial hierarchy. Complementing the complexity results, we propose algorithms for NP-hard extension enforcement based on constrained optimization. Going beyond NP, we propose novel counterexample-guided abstraction refinement procedures for the second-level complete problems and present empirical results on a prototype system constituting the first approach to extension enforcement in its generality.

Strategic argumentation provides a simple model of disputation. We investigate it in the context of Dung's abstract argumentation. We show that strategic argumentation under the grounded semantics is resistant tocorruption -- specifically, collusion and espionage — in a sense similar to Bartholdi et al's notion of a voting scheme resistant to manipulation. Under the stable semantics, strategic argumentation is resistant to espionage, but its resistance to collusion varies according to the aims of the disputants. These results are extended to a variety of concrete languages for argumentation.

Argumentation is a process of evaluating and comparing a set of arguments. A way to compare them consists in using a ranking-based semantics which rank-order arguments from the most to the least acceptable ones. Recently, a number of such semantics have been pro- posed independently, often associated with some desirable properties. However, there is no comparative study which takes a broader perspective. This is what we propose in this work. We provide a general comparison of all these semantics with respect to the proposed proper- ties. That allows to underline the differences of behavior between the existing semantics.