3,549 research outputs found
Stratified Labelings for Abstract Argumentation
We introduce stratified labelings as a novel semantical approach to abstract
argumentation frameworks. Compared to standard labelings, stratified labelings
provide a more fine-grained assessment of the controversiality of arguments
using ranks instead of the usual labels in, out, and undecided. We relate the
framework of stratified labelings to conditional logic and, in particular, to
the System Z ranking functions
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Value-based argumentation frameworks as neural-symbolic learning systems
While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments
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