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Probabilistic epistemic argumentation allows for reasoning aboutargumentation problems in a way that is well founded by probability theory.Epistemic states are represented by probability functions over possible worldsand can be adjusted to new beliefs using update operators. While the use ofprobability functions puts this approach on a solid foundational basis, it alsocauses computational challenges as the amount of data to process dependsexponentially on the number of arguments. This leads to bottlenecks inapplications such as modelling opponent's beliefs for persuasion dialogues. Weshow how update operators over probability functions can be related to updateoperators over much more compact representations that allow polynomial-timeupdates. We discuss the cognitive and probabilistic-logical plausibility ofthis approach and demonstrate its applicability in computational persuasion.

Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation (Technical Report)