Abstract
Towards neuro-argumentative agents based on the seamless integration of neural networks and defeasible formalisms, with principled probabilistic settings and along efficient algorithms, we investigate argumentative Boltzmann machines where the possible states of a Boltzmann machine are constrained by a prior argumentative knowledge. To make our ideas as widely applicable as possible, and acknowledging the role of sub-arguments in probabilistic argumentation, we consider an abstract argumentation framework accounting for sub-arguments, but where the content of (sub-)arguments is left unspecified. We validate our proposal with artificial datasets and suggest its advantages.
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