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  1. A general approach to extension-based semantics in abstract argumentation.Lixing Tan, Zhaohui Zhu & Jinjin Zhang - 2023 - Artificial Intelligence 315 (C):103836.
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  • Shedding new light on the foundations of abstract argumentation: Modularization and weak admissibility.Ringo Baumann, Gerhard Brewka & Markus Ulbricht - 2022 - Artificial Intelligence 310 (C):103742.
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  • An abstract, logical approach to characterizing strong equivalence in non-monotonic knowledge representation formalisms.Ringo Baumann & Hannes Strass - 2022 - Artificial Intelligence 305 (C):103680.
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  • Incremental computation for structured argumentation over dynamic DeLP knowledge bases.Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Gerardo I. Simari & Guillermo R. Simari - 2021 - Artificial Intelligence 300 (C):103553.
    Structured argumentation systems, and their implementation, represent an important research subject in the area of Knowledge Representation and Reasoning. Structured argumentation advances over abstract argumentation frameworks by providing the internal construction of the arguments that are usually defined by a set of (strict and defeasible) rules. By considering the structure of arguments, it becomes possible to analyze reasons for and against a conclusion, and the warrant status of such a claim in the context of a knowledge base represents the main (...)
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  • Handling and measuring inconsistency in non-monotonic logics.Markus Ulbricht, Matthias Thimm & Gerhard Brewka - 2020 - Artificial Intelligence 286 (C):103344.
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  • A general notion of equivalence for abstract argumentation.Ringo Baumann, Wolfgang Dvořák, Thomas Linsbichler & Stefan Woltran - 2019 - Artificial Intelligence 275 (C):379-410.
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  • Strong inconsistency.Gerhard Brewka, Matthias Thimm & Markus Ulbricht - 2019 - Artificial Intelligence 267 (C):78-117.
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  • Methods for solving reasoning problems in abstract argumentation – A survey.Günther Charwat, Wolfgang Dvořák, Sarah A. Gaggl, Johannes P. Wallner & Stefan Woltran - 2015 - Artificial Intelligence 220 (C):28-63.
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  • On the Input/Output behavior of argumentation frameworks.Pietro Baroni, Guido Boella, Federico Cerutti, Massimiliano Giacomin, Leendert van der Torre & Serena Villata - 2014 - Artificial Intelligence 217 (C):144-197.
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  • Normal and strong expansion equivalence for argumentation frameworks.Ringo Baumann - 2012 - Artificial Intelligence 193 (C):18-44.
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  • A principle-based robustness analysis of admissibility-based argumentation semantics.Tjitze Rienstra, Chiaki Sakama, Leendert van der Torre & Beishui Liao - 2020 - Argument and Computation 11 (3):305-339.
    The principle-based approach is a methodology to classify and analyse argumentation semantics. In this paper we classify seven of the main alternatives for argumentation semantics using a set of new robustness principles. These principles complement Baroni and Giacomin’s original classification and deal with the behaviour of a semantics when the argumentation framework changes due to the addition or removal of an attack between two arguments. We distinguish so-called persistence principles and monotonicity principles, where the former deal with the question of (...)
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  • Equivalence in logic-based argumentation.Leila Amgoud, Philippe Besnard & Srdjan Vesic - 2014 - Journal of Applied Non-Classical Logics 24 (3):181-208.
    This paper investigates when two abstract logic-based argumentation systems are equivalent. It defines various equivalence criteria, investigates the links between them, and identifies cases where two systems are equivalent with respect to each of the proposed criteria. In particular, it shows that under some reasonable conditions on the logic underlying an argumentation system, the latter has an equivalent finite subsystem, called core. This core constitutes a threshold under which arguments of the system have not yet attained their final status and (...)
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  • Credulous acceptance in high-order argumentation frameworks with necessities: An incremental approach.Gianvincenzo Alfano, Andrea Cohen, Sebastian Gottifredi, Sergio Greco, Francesco Parisi & Guillermo R. Simari - 2024 - Artificial Intelligence 333 (C):104159.
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