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.

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.

This paper briefly describes the solver ASPrMin, which enumerates preferred extensions and scored first in the Extension Enumeration problem—the only one implemented—of the Preferred Semantics Track of the Second International Competition on Computational Models of Argumentation, ICCMA17.

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.

Big data analytics in healthcare would be almost useless, without suitable tools allowing users "see" them, and gain insight for their situated decisions. The VVH (Valuable Visualization in Healthcare) workshop focuses on the role of interactive data visualization tools by which people can make sense of healthcare data; these data include sensor data, the messages exchanged in social media, the emails between patients and their doctors, the content of patient records as well as the discussions among different specialists that led to such record content. All these data are used by different types of users, like doctors, nurses, policy makers and common citizens. The VVH workshop aims at contributing on: the assessment of the usability of advanced interactive tools of health-related data visualization; the assessment of the quality of the information and value for insight that these tools make available to their users; the collection of reports of either success stories or failures in the appropriation and use of complex and multidimensional healthcare datasets; the collection of methodological and design-oriented contributions that could share methods, techniques, and heuristics for the design of interactive tools and applications supporting data work, data telling and data interpretation in healthcare.

In this paper we describe AFBenchGen2, which allows to randomised argumentation frameworks for testing purposes with a large variety of structures.

In this paper we investigate the impact of automated configuration techniques on the ArgSemSAT solver—runner-up of the ICCMA 2015—for solving the enumeration of preferred extensions. Moreover, we introduce a fully automated method for varying how argumentation frameworks are represented in the input file, and evaluate how the joint configuration of frameworks and ArgSemSAT parameters can have a remarkable impact on performance. Our findings suggest that automated configuration techniques lead to improved performances in argumentation solvers, an important message for participants to the forthcoming competition.

We evaluate the state of the art of solvers for hard argumentation problems—the enumeration of preferred and stable extensions—to envisage future trends based on evidence collected as part of an extensive empirical evaluation. In the last international competition on computational models of argumentation a general impression was that reduction-based systems (either SAT-based or ASP-based) are the most efficient.

jArgSemSAT is a Java re-implementation of ArgSemSAT—a SAT-based solver for abstract argumentation problems—that can be easily integrated in existing argumentation systems (1) as an off-the-shelf, standalone, library; (2) as a Tweety compatible library; and (3) as a fast and robust web service freely available on the Web. Despite being written in Java, jArgSemSAT is very efficient.

Optimization—minimization or maximization—in the lattice of subsets is a frequent operation in Artificial Intelligence tasks. Examples are subset-minimal model-based diagnosis, nonmonotonic reasoning by means of circumscription, or preferred extensions in abstract argumentation. Finding the optimum among many admissible solutions is often harder than finding admissible solutions with respect to both computational complexity and methodology. This paper addresses the former issue by means of an effective method for finding subset-optimal solutions. It is based on the relationship between cardinality-optimal and subset-optimal solutions, and the fact that many logic-based declarative programming systems provide constructs for finding cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or weak constraints in Answer Set Programming (ASP). Clearly each cardinality-optimal solution is also a subset-optimal one, and if the language also allows for the addition of particular restricting constructs (both MaxSAT and ASP do) then all subset-optimal solutions can be found by an iterative computation of cardinality-optimal solutions. As a showcase, the computation of preferred extensions of abstract argumentation frameworks using the proposed method is studied.

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

Abstract argumentation framework (AF) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to AFs are missing, thus limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup.

M.W.A. Caminada, M. Giacomin; Introducing the Special Issue on 20 Years of Argument-Based Inference, Journal of Logic and Computation, Volume 25, Issue 2, 1 Apr

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.

Enumerating semantics extensions in abstract argumentation is generally an intractable problem. For preferred semantics four algorithms have been recently proposed, AspartixM, NAD-Alg, PrefSAT and SCC-P, with significant runtime variations. This work is a first comprehensive exploration of the graph features and of their impact on the execution time of state-of-the-art preferred extensions enumeration algorithms. Following other areas of AI, we exploit empirical performance models, predictive models that relate instance features and algorithms performance. The result is an approach able to select the “best” algorithm for any Dung's argumentation framework with an accuracy, on the average, of the 80%. Moreover, we show that an algorithm selection approach based on classification can select the fastest algorithm in about the double of the number of cases where the most efficient algorithm outperforms the other ones (SCC-P), and about three times the number of cases of the second most efficient algorithm (PrefSAT).

In this paper we describe the system ArgSemSAT which includes algorithms which we proved to overcome current state-of-the-art performances in enumerating preferred extensions.

In this paper we describe the AFBenchGen system, which allows to automatically generate randomised argumentation frameworks for testing purposes.

Semantics extensions are the outcome of the argumentation reasoning process: enumerating them is generally an intractable problem. For preferred semantics two efficient algorithms have been recently proposed, PrefSAT and SCC-P, with significant runtime variations. This preliminary work aims at investigating the reasons (argumentation framework features) for such variations. Remarkably, we observed that few features have a strong impact, and those exploited by the most performing algorithm are not the most relevant.

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.

Starting from the complexity classification of qualitative algebra recently proposed by Jonsson and Krokhin, we study the tractable fragments of Fuzzy Qualitative AlgebraQAfuz, an integrated framework able to deal with qualitative temporal constraints between points and intervals affected by vagueness and uncertainty. To do this we generalize the results obtained for the classical case exploiting the notion of alpha-cut in relating theQAfuztractable fragments to theirQAclassical counterparts. In order to guarantee the applicability of Path-Consistency algorithm, we prove that the identified fragments are algebras. Besides, we also prove that the set of the identified tractable fuzzy fragments is maximal.

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.

An elegant framework called INDU (Pujari et al., Proc. Australian Conf. AI, 1999) has been proposed for representing qualitative information about time intervals and durations. INDU is a single network, therefore it avoids typical problems of bi-networks, and in addition it has interesting computational properties. We extend INDU in two directions: we enrich its expressive power introducing points and maintaining the same computational properties, and we provide it with an axiomatic theory able to handle qualitative temporal information about points, intervals and durations in a unified framework. This theory is based on general interval entities and two relations: "general meets" and "not longer than".