Many-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation
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Abstract (arXiv)
In this paper we propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. The approach allows for conditional reasoning over arguments and boolean combination of arguments, with respect to a class of gradual semantics, through the verification of graded (strict or defeasible) implications over a preferential interpretation. As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning in a many-valued argumentation semantics of weighted argumentation graphs. The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 44 references that are in the map. First posted on arXiv 14 Dec 2022.
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References · 9
- 2020Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)cited by 38
- 2018Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphscited by 23
- 2018Epistemic Graphs for Representing and Reasoning with Positive and Negative Influences of Argumentscited by 14
- 2020Weighted defeasible knowledge bases and a multipreference semantics for a deep neural network modelcited by 11
- 2018On Rational Entailment for Propositional Typicality Logiccited by 7
- 2020On the Semantics of Abstract Argumentation Frameworks: A Logic Programming Approachcited by 6
- 2021A conditional, a fuzzy and a probabilistic interpretation of self-organising mapscited by 6
- 2020Boolean algebras of conditionals, probability and logiccited by 4
- 2022An ASP approach for reasoning on neural networks under a finitely many-valued semantics for weighted conditional knowledge basescited by 4
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