From Structured to Abstract Argumentation: Assumption-Based Acceptance via AF Reasoning

Lehtonen, Tuomo; Wallner, Johannes; Järvisalo, Matti

doi:10.1007/978-3-319-61581-3_6

Cited by 10 publications

(18 citation statements)

References 25 publications

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“…A problem of translation-based systems is that the translation between ABA and AF is itself not trivial in terms of runtime. In fact, there is evidence that the total runtimes of translation-based approaches such as ABA2AF is dominated (Lehtonen et al, 2017). The resulting AF instance may also be much larger than the input ABA framework; even though the complexity of deciding acceptance in AFs and ABA under the logic programming fragment considered in this work coincides for several reasoning tasks-suggesting that there should be efficient (polynomial-time) mappings for translating reasoning tasks in the ABA fragment into AF reasoning tasks-the currently known translations may produce exponentially larger AFs.…”

Section: Systems For Reasoning In Aba and Aba +mentioning

confidence: 99%

“…Dispute derivations for admissible and grounded semantics are implemented in the ABAGRAPH system (Craven & Toni, 2016). The translation-based approach (Dung et al, 2007;Caminada et al, 2013) implemented in the ABA2AF system (Lehtonen et al, 2017) transforms ABA frameworks to AFs, allowing for the use of AF solvers to query credulous and skeptical acceptance under admissible, preferred and stable semantics. Finally, the ABAPLUS system (Bao et al, 2017) for ABA + uses a similar translation, but takes preferences into account in the translation, and supports enumeration of acceptable assumption sets under complete, preferred, stable, grounded and ideal semantics.…”

Section: Introductionmentioning

confidence: 99%

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Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Lehtonen

Wallner

Järvisalo

2021

jair

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The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.

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Section: Systems For Reasoning In Aba and Aba +mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Lehtonen

Wallner

Järvisalo

2021

jair

Self Cite

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“…However, such an endeavor requires care when instantiating: a large number of arguments (relations) should be avoided. We think that more efficient ways of instantiation, for which we already have seen early work [76,103], is a fruitful direction, since both dynamic operators we studied here, and static operators in argumentation can benefit from such efficiency gains.…”

Section: Discussionmentioning

confidence: 94%

“…Regarding the AF, we remark that many current approaches to AF instantiation in structured argumentation may produce large AFs, i.e., AFs whose number of arguments is not polynomially bounded by the size of the knowledge base. Studying approaches to more efficient AF generation is a current topic of research [76,103]. In this paper we make no concrete assumption on the size, but we do assume that both the knowledge base B and the full AF F = (args(B), att(B)) are given, and, further, based on these two given structures that one can in polynomial time check whether an argument (attack) is part of the function args(B) or att(B), and similarly for kb.…”

Section: Complexity Of Structured Enforcement Under Admissible Semanticsmentioning

confidence: 99%

Structural constraints for dynamic operators in abstract argumentation

Wallner

2020

AAC

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Many recent studies of dynamics in formal argumentation within AI focus on the well-known formalism of Dung's argumentation frameworks (AFs). Despite the usefulness of AFs in many areas of argumentation, their abstract notion of arguments creates a barrier for operators that modify a given AF, e.g., in the case that dependencies between arguments have been abstracted away that are important for subsequent modifications. In this paper we aim to support development of dynamic operators on formal models in abstract argumentation by providing constraints imposed on the modification of the structure that can be used to incorporate information that has been abstracted away. Towards a broad reach, we base our results on the general formalism of abstract dialectical frameworks (ADFs) in abstract argumentation. To show applicability, we present two cases studies that adapt an existing extension enforcement operator that modifies AFs: in the first case study, we show how to utilize constraints in order to obtain an enforcement operator on ADFs that is allowed to only add support relations between arguments, and in the second case study we show how an enforcement operator on AFs can be defined that respects dependencies between arguments. We show feasibility of our approach by studying the complexity of the proposed structural constraints and the operators arising from the case studies, and by an experimental evaluation of an answer set programming (ASP) implementation of the enforcement operator based on supports.

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“…ABA problems are one of the prevalent forms of structured argumentation in which, differently from AFs, the internal structure of arguments is made explicit through derivations from more basic structure (Toni, 2014). The translation employed is described in (Lehtonen et al, 2017). The original ABA set contains randomly generated cyclic and acyclic ABAs that, after a selection from the authors, resulted in a total of 426 instances.…”

Section: New Domainsmentioning

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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From Structured to Abstract Argumentation: Assumption-Based Acceptance via AF Reasoning

Cited by 10 publications

References 25 publications

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Structural constraints for dynamic operators in abstract argumentation

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

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