Abstract
The Bipolar Argumentation Framework approach is an extension of the Abstract Argumentation Framework. A Bipolar Argumentation Framework considers a support interaction between arguments, besides the attack interaction. As in the Abstract Argumentation Framework, some researches consider that arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained from a Bipolar Argumentation Framework under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real application domains there is a need for aggregating probability values from different sources so it is not suitable to aggregate such probability values in a unique probability distribution. To tackle this challenge, we use credal networks theory for modelling the uncertainty of the degree of belief of arguments in a BAF. We also propose an algorithm for calculating the degree of uncertainty of the extensions inferred by a given argumentation semantics. Moreover, we introduce the idea of modelling the support relation as a causal relation. We formally show that the introduced approach is sound and complete w.r.t the credal networks theory.
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Morveli-Espinoza, M., Nieves, J.C. & Tacla, C.A. Probabilistic causal bipolar abstract argumentation: an approach based on credal networks. Ann Math Artif Intell 91, 517–536 (2023). https://doi.org/10.1007/s10472-023-09851-4
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DOI: https://doi.org/10.1007/s10472-023-09851-4