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.

In Dung's abstract argumentation, arguments are either acceptable or unacceptable, given a chosen notion of acceptability. This gives a coarse way to compare arguments. In this paper, we propose a counting approach for a more fine-gained assessment to arguments by counting the number of their respective attackers and defenders based on argument graph and argument game. An argument is more acceptable if the proponent puts forward more number of defenders for it and the opponent puts forward less number of attackers against it. We show that our counting model has two well-behaved properties: normalization and convergence. Then, we define a counting semantics based on this model, and investigate some general properties of the semantics.

This paper presents the STAR system, a system for automated narrative comprehension, developed on top of an argumentation-theoretic formulation of defeasible reasoning, and strongly following guidelines from the psychology of comprehension. We discuss the system's use in psychological experiments on story comprehension, and our plans for its broader use in empirical studies concerning wider issues of commonsense reasoning.

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 AF s 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.

Argumentation Theory and Answer Set Programming (ASP) are two prominent theories in the field of knowledge representation and non-monotonic reasoning,where Argumentation Theory stands for a variety of approaches following similar ideas.The main difference between Argumentation Theory and ASP is that the former focusses on representing knowledge and reasoning about it in a way that resembles human reasoning, neglecting the efficiency of the reasoning procedure,whereas the latter is concerned with the efficient computation of solutions to a reasoning problem, resulting in a less human-understandable process. In recent years, ASP has been frequently applied for the computation of reasoning problems represented in argumentation-theoretical-terms and has been found an efficient method for determining solutions to problems in Argumentation Theory. My research is concerned with the opposite direction, i.e. with applying Argumentation Theory to ASP in order to explain the solutions to an ASP reasoning problem in a more human-understandable way.Developing such an explanation method also involves to investigate both the exact relationship between different approaches in Argumentation Theory in order to find the most suitable one for explanations and their connection with ASP, in particular with respect to their semantics.

Since Assumption-Based Argumentation (ABA) was introduced in the nineties,the structure and semantics of an ABA framework have been studied exclusively in logical termswithout any graphical representation.Here, we show how an ABA framework and its complete semantics can be displayed in a graph,clarifying the structure of the ABA framework as well as the resulting complete assumption labellings.Furthermore, we show that such an ABA graph can be used to represent the structureand semantics of a logic program (LP), based on the correspondence between the semantics of a LP and an ABA framework encoding this LP.

Argumentation can be viewed as a process of generating explanations. However, existing argumentation semantics are developed for identifying acceptable arguments within a set, rather than giving concrete justifications for them. In this work, we propose a new argumentation semantics, related admissibility, designed for giving explanations to arguments in both Abstract Argumentation and Assumption-based Argumentation. We identify different types of explanations defined in terms of the new semantics. We also give a correct computational counterpart for explanations using dispute forests.

Argumentative discussion is a highly demanding task. In order to help people in such situations, this paper provides an innovative methodology for developing an agent that can support people in argumentative discussions by proposing possible arguments to them. By analyzing more than 130 human discussions and 140 questionnaires, answered by people, we show that the well-established Argumentation Theory is not a good predictor of people's choice of arguments. Then, we present a model that has 76% accuracy when predicting people’s top three argument choices given a partial deliberation. We present the Predictive and Relevance based Heuristic agent (PRH), which uses this model with a heuristic that estimates the relevance of possible arguments to the last argument given in order to propose possible arguments. Through extensive human studies with over 200 human subjects, we show that people’s satisfaction from the PRH agent is significantly higher than from other agents that propose arguments based on Argumentation Theory, predict arguments without the heuristics or only the heuristics. People also use the PRH agent's proposed arguments significantly more often than those proposed by the other agents.

Given the fast rise of increasingly autonomous artificial agents and robots, a key acceptability criterion 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 persuasive acts themselves (e.g. “is it morally acceptable that a machine lies or appeals to the emotions of a person to persuade her, even if for a good end?”). Exploiting a behavioral approach, based on human assessment of moral dilemmas – i.e. without any prior assumption of underlying ethical theories – this paper reports on a set of experiments. These experiments address the type of persuader (human or machine), the strategies adopted (purely argumentative, appeal to positive emotions, appeal to negative emotions, lie) and the circumstances. Findings display no differences due to the agent, mild acceptability for persuasion and reveal that truth-conditional reasoning (i.e. argument validity) is a significant dimension affecting subjects’ judgment. Some implications for the design of intelligent persuasive systems are discussed.

In this paper we will be concerned with such reasoning in its most general form, that is, in inferences that are defeasible: given more information, we may retract them. The purpose of this paper is to introduce a form of non-monotonic inference based on the notion of a partial model of the world. We take partial models to reflect our partial knowledge of the true state of affairs. We then define non-monotonic inference as the process of filling in unknown parts of the model with conjectures: statements that could turn out to be false, given more complete knowledge. To take a standard example from default reasoning: since most birds can fly, if Tweety is a bird it is reasonable to assume that she can fly, at least in the absence of any information to the contrary. We thus have some justification for filling in our partial picture of the world with this conjecture. If our knowledge includes the fact that Tweety is an ostrich, then no such justification exists, and the conjecture must be retracted.