Abstract In this paper, we introduce a general-purpose model to categorize, quantify and analyse the impact of public interest communication on target audiences. The model maps the central elements of a communication campaign such as the information flow it generates and the interaction with the beliefs, values and expectations of its target audiences. Since communication campaigns are based on arguments, we leverage on formal argumentation to specify its main ingredients and to understand the multifaceted ways a campaign can impact the beliefs of a target audience. As its main contribution, this work introduces a novel quantitative framework for value-based argumentation, in line with mainstream theories of human values, where vectors are employed to represent a multi-dimensional spectrum of values influencing audience perception and response.

We present a conceptualization providing an original domain-independent perspective on two crucial properties in reasoning: consistency and reinstatement. They emerge as a pair of dual characteristics, representing complementary requirements on the outcomes of reasoning processes. Central to our formalization are two underlying parametric relations: incompatibility and reinstatement violation. Different instances of these relations give rise to a spectrum of consistency and reinstatement scenarios. As a demonstration of versatility and expressive power of our approach we provide a characterization of various abstract argumentation semantics which are expressed as combinations of distinct consistency and reinstatement constraints. Moreover, we conduct an investigation into preserving these essential properties across different reasoning stages. Specifically, we delve into scenarios where a labelling is derived from other labellings through a synthesis function, using the synthesis of argument justification as an illustrative instance. We achieve a general characterization of consistency preservation synthesis functions, while we unveil an impossibility result concerning reinstatement preservation, leading us to explore an alternative notion to ensure feasibility. Our exploration reveals a weakness in the traditional definition of argument justification, for which we propose a refined version overcoming this limitation.

This special issue of the Journal of Logic and Computation presents 11 carefully selected articles in the field of Logic and Argumentation. Authors of the strongest contributions to 4th International Conference on Logic and Argumentation (CLAR 2021) were invited to submit extended versions of their papers for inclusion in this special issue. All articles in this volume were carefully reviewed by two or three expert reviewers before acceptance was finally granted. CLAR 2021 was held in Hangzhou, 20–22 October 2021 at the Zhejiang University City College in China. This special issue was made possible by the numerous strong submissions (58 in total) we received for the conference, from which 30 presentations and 4 additional invited talks were selected for presentation at the conference and for publication in the CLAR 2021 proceedings published as volume 13040 in the Springer series Lecture Notes in Computer Science. The contributions to this special issue, and more generally to CLAR 2021, cover a wide range of topics that are the focus of the CLAR conference series, including in particular abstract and structured argumentation, logic and argumentation, argumentation with qualitative and quantitative uncertainty, knowledge representation and reasoning, modal and non-classical logics and non-monotonic logics, with applications in a variety of fields like law, ethics, game theory, linguistics and medical reasoning.

We propose a generic notion of consistency in an abstract labelling setting, based on two relations: one of intolerance between the labelled elements and one of incompatibility between the labels assigned to them, thus allowing a spectrum of consistency requirements depending on the actual choice of these relations. As a first application to formal argumentation, we show that traditional Dung’s semantics can be put in correspondence with different consistency requirements in this context. We consider then the issue of consistency preservation when a labelling is obtained as a synthesis of a set of labellings, as is the case for the traditional notion of argument justification. In this context we provide a general characterization of consistency-preserving synthesis functions and analyze the case of argument justification in this respect.

Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years. Among various approaches to XAI, argumentative models have been advocated in both the AI and social science literature, as their dialectical nature appears to match some basic desirable features of the explanation activity. In this survey we overview XAI approaches built using methods from the field of computational argumentation, leveraging its wide array of reasoning abstractions and explanation delivery methods. We overview the literature focusing on different types of explanation (intrinsic and post-hoc), different models with which argumentation-based explanations are deployed, different forms of delivery, and different argumentation frameworks they use. We also lay out a roadmap for future work.

We propose a general method for generating counterfactual explanations (CFXs) for a range of Bayesian Network Classifiers (BCs), e.g. single- or multi-label, binary or multidimensional. We focus on explanations built from relations of (critical and potential) influence between variables, indicating the reasons for classifications, rather than any probabilistic information. We show by means of a theoretical analysis of CFXs’ properties that they serve the purpose of indicating (potentially) pivotal factors in the classification process, whose absence would give rise to different classifications. We then prove empirically for various BCs that CFXs provide useful information in real world settings, e.g. when race plays a part in parole violation prediction, and show that they have inherent advantages over existing explanation methods in the literature.

In computational models of argumentation, the justification of statements has drawn less attention than the construction and justification of arguments. Significant losses of sensitivity or expressibility on statement statuses can be incurred by otherwise appealing formalisms. In order to reappraise statement statuses and, more generally, to support a uniform modelling of different phases of the argumentation process we introduce multi-labelling systems, a generic formalism devoted to represent reasoning processes consisting of a sequence of labelling stages. In the argumentation context, two families of multi-labelling systens, called argument-focused and statement-focused approach are identified and compared. Then they are shown to be able to encompass several prominent literature proposals as special cases, thereby enabling a systematic comparison evidencing their merits and limits. Further, we show that the proposed model supports tunability of statement justification by specifying a few alternative statement justification labellings, and we illustrate how they can be seamlessly integrated into different formalisms.

The study of properties of gradual evaluation methods in argumentation has received increasing attention in recent years, with studies devoted to various classes of frameworks/methods leading to conceptually similar but formally distinct properties in different contexts. In this paper we provide a systematic analysis for this research landscape by making three main contributions. First, we identify groups of conceptually related properties in the literature, which can be regarded as based on common patterns and, using these patterns, we evidence that many further properties can be considered. Then, we provide a simplifying and unifying perspective for these properties by showing that they are all implied by the parametric principles of (either strict or non-strict) balance and monotonicity. Finally, we show that (instances of) these principles are satisfied by several quantitative argumentation formalisms in the literature, thus confirming their general validity and their utility to support a compact, yet comprehensive, analysis of properties of gradual argumentation.

This demo presents the web system “Games of Argumentation”, which allows users to build argumentation graphs and examine them in a game-theoretical manner using up to three different evaluation techniques. The concurrent evaluations of arguments using different techniques, which may be qualitative or quantitative, provides a significant aid to users in both understanding game-theoretical argumentation semantics and pinpointing their differences from alternative semantics, traditional or otherwise, to differentiate between them.

This discussion paper describes the “Laboratory of Dilemmas”, a paradigmatic decision problem presented as a video installation at La Biennale of Venice, and discusses the challenges it poses for argument-based models of decision making. It then sketches and compares two investigation directions to address these challenges and provides some relevant preliminary technical observations.

In computational models of argumentation, argument justification has attracted more attention than statement justification, and significant sensitivity losses are identifiable when dealing with the justification of statements by otherwise appealing formalisms. This paper reappraises statement justification as a formalism-independent component in argument-based reasoning. We introduce a novel general model of argument-based reasoning based on multiple stages of labellings, the last one being devoted to statement justification, identify two alternative paths from argument acceptance to statement justification, and compare their expressiveness. We then show that this model encompasses several prominent literature proposals as special cases, thereby enabling a systematic comparison of existing approaches to statement justification, evidencing their merits and limits. Finally we illustrate our model by specifying a generic ignorance-aware statement justification and showing how it can be seamlessly integrated into different formalisms.

Pietro Baroni, Dov M. Gabbay, Massimiliano Giacomin; Introduction to the special issue on Loops in Argumentation, Journal of Logic and Computation, Volume

Epistemic probabilities in argumentation frameworks are meant to represent subjective degrees of belief in the acceptance of arguments. As such, they are subject to some rationality conditions, taking into account the attack relation between arguments. This paper provides an advancement with respect to the previous literature on this matter by casting epistemic probabilities in the context of de Finetti's theory of subjective probability and by analyzing and revising the relevant rationality properties in relation with de Finetti's notion of coherence. Further, we consider the extension to Walley's theory of imprecise probabilities and carry out a preliminary analysis about rationality conditions in this more general context.

This paper introduces Input/Output Argumentation Frameworks, a novel approach to characterize the behavior of an argumentation framework as a sort of black box exposing a well-defined external interface. As a starting point, we define the novel notion of semantics decomposability and analyze complete, stable, grounded and preferred semantics in this respect. Then we show as a main result that, under grounded, complete, stable and credulous preferred semantics, Input/Output Argumentation Frameworks with the same behavior can be interchanged without affecting the result of semantics evaluation of other arguments interacting with them.

We consider the problem of counting (without explicitly enumerating) extensions prescribed by multiple-status semantics in abstract argumentation. Referring to Dung's traditional stable and preferred semantics and to the recently introduced resolution-based grounded semantics (GR*), we show that in general extension counting is computationally hard (actually #P-complete). We then identify non-trivial topological classes of argumentation frameworks where extension counting is tractable. In particular we show, by providing and analyzing the relevant algorithms, that in symmetric argumentation frameworks counting GR* extensions is tractable (but is still hard for stable and preferred estensions), while counting is tractable for all the considered semantics in tree-like argumentation frameworks.

Relationships between risk measures and imprecise probability theory have received relatively limited attention in the literature. This paper contributes to filling this gap as far as Dutch risk measures are concerned. Using imprecise previsions as a starting point, a novel generalized family of Dutch risk measures is introduced, its properties with respect to several alternative consistency notions are analyzed and its advantageous features discussed. Any such measure corresponds to an imprecise prevision correcting a first-approach uncertainty measure, while preserving its consistency properties in several cases, ranging from coherence to a weak generalization of the concept of capacity. Further, it is shown that the proposed family of measures has a practical significance in the application context of insurance pricing, since, unlike the original formulation, it may ensure a risk loading to each risk taker and is even compatible with the practice of double loading in premium policies.

This paper presents an approach to support the designer of Visual Interactive Systems (VISs) in adapting a VIS to the evolution of its users. This process is called co-evolution of users and systems. The approach is based on the identification of the patterns of interaction between the user and an interactive system and on their use for the evolution of the system to facilitate novel usages introduced by the user. The approach is focused on WIMP systems and is based on the recently introduced PCL (Pictorial Computing Laboratory) model of interaction, within which we provide a novel definition of interaction pattern. The proposal assumes that the VIS is observed by an external system called SIC (Supporting Interaction Co-evolution), which is in charge of recording the interactions between the user and the VIS and of analyzing the relevant interaction patterns. In particular, SIC exploits a UML-based statechart specification of the VIS in order to associate observed user activities with the states of the interactive process. This information provides a useful basis for a variety of pattern recognition techniques. Two techniques called usual state and recurrent sequence recognition are illustrated and the results of a first experiment are discussed. © 2009 ACM.

Argumentation is receiving an increasing attention as a technique for practical and uncertain reasoning underlying the realization of intelligent autonomous agents. Since a decentralized organization has been proposed by several authors as an appropriate paradigm for the design of agent architectures, we propose in this article, a distributed approach to argumentation, in which several independent asynchronous processes carry out argumentation activity, by exploiting local information only. The final result of this process is the computation of the defeat status of the arguments: we devise a general distributed algorithm, which does not rely on any specific notion of defeat between arguments. The issue of coordination has been explicitly tackled by ensuring the property of self-stabilization for the algorithm. A proof of its correctness, as well as an analysis of its complexity, is provided.

We propose a distributed approach to argumentation in which arguments are constructed by asynchronous processes which compute their own defeat status on the basis of local information. We present a self-stabilizing algorithm for the computation of defeat status, along with a proof of its correctness. The proposed approach introduces a remarkable flexibility in the management of argumentation activity with respect to a centralized approach. Moreover it is in line with the evolution of autonomous agents architectures, one of the main application fields of argumentation systems.

Discrete-event modeling can be applied to a large variety of physical systems, in order to support different tasks, including fault detection, monitoring, and diagnosis. The paper focuses on the model-based diagnosis of a class of distributed discrete-event systems, called active systems. An active system, which is designed to react to possibly harmful external events, is modeled as a network of communicating automata, where each automaton describes the behavior of a system component. Unlike other approaches based on the synchronous composition of automata and on the off-line creation of the model of the entire system, the proposed diagnostic technique deals with asynchronous events and does not need any global diagnoser to be built. Instead, the current approach features a problem-decomposition/solution-composition nature whose core is the online progressive reconstruction of the behavior of the active system, guided by the available observations. This incremental technique makes effective the diagnosis of large-scale active systems, for which the one-shot generation of the global model is almost invariably impossible in practice. The diagnostic method encompasses three steps: (1) reconstruction planning; (2) behavior reconstruction; and (3) diagnosis generation. Step 1 draws a hierarchical decomposition of the behavior reconstruction problem. Reconstruction is made in Step 2, where an intensional representation of all the dynamic behaviors which are consistent with the available system observation is produced. Diagnosis is eventually generated in Step 3, based on the faulty evolutions incorporated within the reconstructed behaviors. The modular approach is formally defined, with special emphasis on Steps 2 and 3, and applied to the power transmission network domain.

Uncertainty calculus overcomes many of the drawbacks affecting other well-known approaches to uncertain reasoning. Nevertheless, it suffers from other severe limitations and gives rise to counter-intuitive results. We show that common sense inconsistencies of uncertainty calculus originate from a scarce cognitive plausibility in the definition of propagation and aggregation operators, and we propose an improved version of Driankov's uncertainty calculus (1986) where the definition of such operators is substantially revised according to common sense criteria. Revised uncertainty calculus allows overcoming in a general and uniform way all the problems identified in uncertainty calculus and provides a substantial enhancement of cognitive plausibility. It has been successfully tested in the design and development of a real application concerning preventive diagnosis of power transformers.