Toward Artificial Argumentation

Atkinson, Katie; Baroni, Pietro; Giacomin, Massimiliano; Hunter, Anthony; Prakken, Henry; Reed, Chris; Simari, Guillermo Ricardo; Thimm, Matthias; Villata, Serena

doi:10.1609/aimag.v38i3.2704

Cited by 154 publications

(129 citation statements)

References 24 publications

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“…These include decision support, knowledge representation, nonmonotonic reasoning, and multiagent systems. Moreover, computational argumentation provides a formal investigation of problems that have been studied informally only by philosophers, and which consequently allow for the development of computational tools for argumentation, see (Atkinson et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Design and results of the Second International Competition on Computational Models of Argumentation

Gaggl

Linsbichler

Maratea

et al. 2020

Artificial Intelligence

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Argumentation is a major topic in the study of Artificial Intelligence. Since the first edition in 2015, advancements in solving (abstract) argumentation frameworks are assessed in competition events, similar to other closely related problem solving technologies. In this paper, we report about the design and results of the Second International Competition on Computational Models of Argumentation, which has been jointly organized by TU Dresden (Germany), TU Wien (Austria), and the University of Genova (Italy), in affiliation with the 2017 International Workshop on Theory and Applications of Formal Argumentation. This second edition maintains some of the design choices made in the first event, e.g. the I/O formats, the basic reasoning problems, and the organization into tasks and tracks. At the same time, it introduces significant novelties, e.g. three additional prominent semantics, and an instance selection stage for classifying instances according to their empirical hardness. $ This paper is an extended and revised version of a paper presented at the First International Workshop on Systems and Algorithms for Formal Argumentation (Gaggl et al., 2016), which included the design of the event before the competition was run. A brief survey of the competition is to be published in AI Magazine (Gaggl et al., 2018).

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Section: Introductionmentioning

confidence: 99%

Design and results of the Second International Competition on Computational Models of Argumentation

Gaggl

Linsbichler

Maratea

et al. 2020

Artificial Intelligence

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“…This paper proposes a novel paradigm (that we call Ar-gOpt) for explaining why a solution is (not) good by leveraging abstract argumentation (AA) as an intermediate layer between the user and optimization software. Argumentation is highly suitable for explaining reasoning and decisions (Moulin et al 2002;Atkinson et al 2017) with argumentative explanations proposed in various settings, see e.g. (García et al 2013;Fan and Toni 2015;Čyras et al 2018;Zeng et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Argumentation for Explainable Scheduling

Čyras

Letsios

Misener

et al. 2019

AAAI

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Mathematical optimization offers highly-effective tools for finding solutions for problems with well-defined goals, notably scheduling. However, optimization solvers are often unexplainable black boxes whose solutions are inaccessible to users and which users cannot interact with. We define a novel paradigm using argumentation to empower the interaction between optimization solvers and users, supported by tractable explanations which certify or refute solutions. A solution can be from a solver or of interest to a user (in the context of ‘what-if’ scenarios). Specifically, we define argumentative and natural language explanations for why a schedule is (not) feasible, (not) efficient or (not) satisfying fixed user decisions, based on models of the fundamental makespan scheduling problem in terms of abstract argumentation frameworks (AFs). We define three types of AFs, whose stable extensions are in one-to-one correspondence with schedules that are feasible, efficient and satisfying fixed decisions, respectively. We extract the argumentative explanations from these AFs and the natural language explanations from the argumentative ones.

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“…Formal argumentation is a vibrant field within AI. On the one hand it provides genuine methods to model discourses or legal cases (Atkinson et al 2017). On the other hand, it is closely related to -and gives an orthogonal view onseveral formalisms from the AI domain, e.g.…”

Section: Introductionmentioning

confidence: 99%

Complexity of Abstract Argumentation under a Claim-Centric View

Dvořák

Woltran

2019

AAAI

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argumentation frameworks have been introduced by Dung as part of an argumentation process, where arguments and conflicts are derived from a given knowledge base. It is solely this relation between arguments that is then used in order to identify acceptable sets of arguments. A final step concerns the acceptance status of particular statements by reviewing the actual contents of the acceptable arguments. Complexity analysis of abstract argumentation so far has neglected this final step and is concerned with argument names instead of their contents, i.e. their claims. As we outline in this paper, this is not only a slight deviation but can lead to different complexity results. We, therefore, give a comprehensive complexity analysis of abstract argumentation under a claim-centric view and analyse the four main decision problems under seven popular semantics. In addition, we also address the complexity of common sub-classes and introduce novel parameterisations – which exploit the nature of claims explicitly – along with fixed-parameter tractability results.

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Toward Artificial Argumentation

Cited by 154 publications

References 24 publications

Design and results of the Second International Competition on Computational Models of Argumentation

Design and results of the Second International Competition on Computational Models of Argumentation

Argumentation for Explainable Scheduling

Complexity of Abstract Argumentation under a Claim-Centric View

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