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  1. An abstract and structured account of dialectical argument strength.Henry Prakken - 2024 - Artificial Intelligence 335 (C):104193.
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  • Belief Revision and Computational Argumentation: A Critical Comparison.Pietro Baroni, Eduardo Fermé, Massimiliano Giacomin & Guillermo Ricardo Simari - 2022 - Journal of Logic, Language and Information 31 (4):555-589.
    This paper aims at comparing and relating belief revision and argumentation as approaches to model reasoning processes. Referring to some prominent literature references in both fields, we will discuss their (implicit or explicit) assumptions on the modeled processes and hence commonalities and differences in the forms of reasoning they are suitable to deal with. The intended contribution is on one hand assessing the (not fully explored yet) relationships between two lively research fields in the broad area of defeasible reasoning and (...)
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  • A computational model of argumentation schemes for multi-agent systems.Alison R. Panisson, Peter McBurney & Rafael H. Bordini - 2021 - Argument and Computation 12 (3):357-395.
    There are many benefits of using argumentation-based techniques in multi-agent systems, as clearly shown in the literature. Such benefits come not only from the expressiveness that argumentation-based techniques bring to agent communication but also from the reasoning and decision-making capabilities under conditions of conflicting and uncertain information that argumentation enables for autonomous agents. When developing multi-agent applications in which argumentation will be used to improve agent communication and reasoning, argumentation schemes are useful in addressing the requirements of the application domain (...)
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  • A top-level model of case-based argumentation for explanation: Formalisation and experiments.Henry Prakken & Rosa Ratsma - 2022 - Argument and Computation 13 (2):159-194.
    This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the (...)
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  • Computational complexity of flat and generic Assumption-Based Argumentation, with and without probabilities.Kristijonas Čyras, Quentin Heinrich & Francesca Toni - 2021 - Artificial Intelligence 293 (C):103449.
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  • A general semi-structured formalism for computational argumentation: Definition, properties, and examples of application.Pietro Baroni, Massimiliano Giacomin & Beishui Liao - 2018 - Artificial Intelligence 257 (C):158-207.
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  • Argument graphs and assumption-based argumentation.Robert Craven & Francesca Toni - 2016 - Artificial Intelligence 233 (C):1-59.
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  • On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games: 25 years later.Pietro Baroni, Francesca Toni & Bart Verheij - 2020 - Argument and Computation 11 (1-2):1-14.
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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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  • Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines.Kristijonas Čyras, Tiago Oliveira, Amin Karamlou & Francesca Toni - 2021 - Argument and Computation 12 (2):149-189.
    A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with (...)
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  • Structural constraints for dynamic operators in abstract argumentation.Johannes P. Wallner - 2020 - Argument and Computation 11 (1-2):151-190.
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  • Merging operators on stratified belief bases equipped with argumentative inference.Marcelo A. Falappa, Alejandro J. García & Guillermo R. Simari - 2023 - Journal of Applied Non-Classical Logics 33 (3-4):387-420.
    This work considers the formalisation of the merging process of stratified belief bases, where beliefs are stored in different layers or strata. Their strata are ranked, following a total order, employing the value the agent using the belief base assigns to these beliefs. The agent uses an argumentation mechanism to reason from the belief base and obtain the final inferences. We present two ways of merging stratified belief bases: the first is defined by merging two strata without belief preservation, and (...)
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  • Yes, no, maybe, I don’t know: Complexity and application of abstract argumentation with incomplete knowledge.Jean-Guy Mailly - 2022 - Argument and Computation 13 (3):291-324.
    argumentation, as originally defined by Dung, is a model that allows the description of certain information about arguments and relationships between them: in an abstract argumentation framework, the agent knows for sure whether a given argument or attack exists. It means that the absence of an attack between two arguments can be interpreted as “we know that the first argument does not attack the second one”. But the question of uncertainty in abstract argumentation has received much attention in the last (...)
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  • A Structured Argumentation Framework for Modeling Debates in the Formal Sciences.Marcos Cramer & Jérémie Dauphin - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (2):219-241.
    Scientific research in the formal sciences comes in multiple degrees of formality: fully formal work; rigorous proofs that practitioners know to be formalizable in principle; and informal work like rough proof sketches and considerations about the advantages and disadvantages of various formal systems. This informal work includes informal and semi-formal debates between formal scientists, e.g. about the acceptability of foundational principles and proposed axiomatizations. In this paper, we propose to use the methodology of structured argumentation theory to produce a formal (...)
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  • On searching explanatory argumentation graphs.Régis Riveret - 2020 - Journal of Applied Non-Classical Logics 30 (2):123-192.
    Cases or examples can be often explained by the interplay of arguments in favour or against their outcomes. This paper addresses the problem of finding explanations for a collection of cases where an explanation is a labelled argumentation graph consistent with the cases, and a case is represented as a statement labelling. The focus is on semi-abstract argumentation graphs specifying attack and subargument relations between arguments, along with particular complete argument labellings taken from probabilistic argumentation where arguments can be excluded. (...)
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  • Syntactic reasoning with conditional probabilities in deductive argumentation.Anthony Hunter & Nico Potyka - 2023 - Artificial Intelligence 321 (C):103934.
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  • The Elusive Notion of “Argument Quality”.Michael H. G. Hoffmann - 2018 - Argumentation 32 (2):213-240.
    We all seem to have a sense of what good and bad arguments are, and there is a long history—focusing on fallacies—of trying to provide objective standards that would allow a clear separation of good and bad arguments. This contribution discusses the limits of attempts to determine the quality of arguments. It begins with defining bad arguments as those that deviate from an established standard of good arguments. Since there are different conceptualizations of “argument”—as controversy, as debate, and as justification—and (...)
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  • An informant-based approach to argument strength in Defeasible Logic Programming.Andrea Cohen, Sebastian Gottifredi, Luciano H. Tamargo, Alejandro J. García & Guillermo R. Simari - 2021 - Argument and Computation 12 (1):115-147.
    This work formalizes an informant-based structured argumentation approach in a multi-agent setting, where the knowledge base of an agent may include information provided by other agents, and each piece of knowledge comes attached with its informant. In that way, arguments are associated with the set of informants corresponding to the information they are built upon. Our approach proposes an informant-based notion of argument strength, where the strength of an argument is determined by the credibility of its informant agents. Moreover, we (...)
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  • A Generalized Proof-Theoretic Approach to Logical Argumentation Based on Hypersequents.AnneMarie Borg, Christian Straßer & Ofer Arieli - 2020 - Studia Logica 109 (1):167-238.
    In this paper we introduce hypersequent-based frameworks for the modelling of defeasible reasoning by means of logic-based argumentation and the induced entailment relations. These structures are an extension of sequent-based argumentation frameworks, in which arguments and the attack relations among them are expressed not only by Gentzen-style sequents, but by more general expressions, called hypersequents. This generalization allows us to overcome some of the known weaknesses of logical argumentation frameworks and to prove several desirable properties of the entailments that are (...)
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  • Acquiring knowledge from expert agents in a structured argumentation setting.Ramiro Andres Agis, Sebastian Gottifredi & Alejandro Javier García - 2019 - Argument and Computation 10 (2):149-189.
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