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

Gaggl, Sarah Alice; Linsbichler, Thomas; Maratea, Marco; Woltran, Stefan

doi:10.1016/j.artint.2019.103193

Cited by 42 publications

(42 citation statements)

References 53 publications

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“…Various algorithms for reasoning in different argumentation formalisms exist (Cerutti et al, 2018). Compared to the number of algorithms and system implementations recently developed for reasoning in abstract argumentation frameworks (Charwat et al, 2015), motivated also by the ICCMA competition series Gaggl et al, 2020), fewer system implementations are available for reasoning in structured argumentation formalisms. The algorithmic approaches implemented by different systems can be divided into specialized algorithmic approaches and reductionbased approaches which are based on declarative encodings of the reasoning problems at hand.…”

Section: Algorithmic Approaches To Reasoning In Abstract and Structured Formalismsmentioning

confidence: 99%

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: Algorithmic Approaches To Reasoning In Abstract and Structured Formalismsmentioning

confidence: 99%

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Lehtonen

Wallner

Järvisalo

2021

jair

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“…We also open new, exciting possibilities in the area of learning for improving performance of abstract argumenta-tion solvers. We believe this work would be particularly beneficial for the participants of the forthcoming competition ICCMA 2017 [40]. 9 We also exploit the ICCMA-15 legacy by combining state-of-the-art solvers, able to handle EE-PR and EE-ST problems, using portfolio-based techniques.…”

Section: Resultsmentioning

confidence: 99%

On the impact of configuration on abstract argumentation automated reasoning

Cerutti

Vallati

Giacomin

2018

International Journal of Approximate Reasoning

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In this paper we consider the impact of configuration of abstract argumentation reasoners both when using a single solver and choosing combinations of framework representation-solver options; and also when composing portfolios of algorithms. To exemplify the impact of the framework-solver configuration we consider one of the most configurable solvers, namely ArgSemSAT-runner-up of the last competition on computational models of argumentation (ICCMA-15)-for enumerating preferred extensions. We discuss how to configure the representation of the argumentation framework in the input file and show how this coupled framework-solver configuration can have a remarkable impact on performance. As to the impact of configuring differently structured portfolios of abstract argumentation solvers, we consider the solvers submitted to ICCMA-15, which provided the community with a heterogeneous panorama of approaches for handling abstract argumentation frameworks. A superficial reading of the results of ICCMA-15 is that reduction-based systems (either SAT-based or ASP-based) are always the most efficient. Our investigation, concerning the enumeration of stable and preferred extensions, shows that this is not true in full generality and suggests the areas where the relatively underdeveloped non reduction-based systems should focus more to improve their performance. Moreover, it also highlights that the state-of-the-art solvers are very complementary and can be successfully combined in portfolios.

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“…The paper induces several future work directions. We believe the most exciting ones are: i) algorithm design and implementation of our approach which allow to cope with acceptability in terms of complete semantics in static as well as dynamic environments (Gaggl et al 2020), ii) an in-depth comparison to other notions of explanations, and iii) investigate whether this decomposition of extensions works analogously for other related formalisms as well, e.g. Answer Set Programming or Reiter's Default Logic.…”

Section: Discussionmentioning

confidence: 99%

Choices and their Consequences - Explaining Acceptable Sets in Abstract Argumentation Frameworks

Baumann

Ulbricht

2021

Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning

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We develop a notion of explanations for acceptance of arguments in an abstract argumentation framework. To this end we show that extensions returned by Dung's standard semantics can be decomposed into i) non-deterministic choices made on even cycles of the given argumentation graph and then ii) deterministic iteration of the so-called characteristic function. Naturally, the choice made in i) can be viewed as an explanation for the corresponding extension and thus the arguments it contains. We proceed to propose desirable criteria a reasonable notion of an explanation should satisfy. We present an exhaustive study of the newly introduced notion w.r.t. these criteria. Finally some interesting decision problems arise from our analysis and we examine their computational complexity, obtaining some surprising tractability results.

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

Cited by 42 publications

References 53 publications

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

On the impact of configuration on abstract argumentation automated reasoning

Choices and their Consequences - Explaining Acceptable Sets in Abstract Argumentation Frameworks

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