Online event. November 3-12, 2021.
Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization
Abstract argumentation is a prominent reasoning framework.
It comes with a variety of semantics, and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation.
While admissibility is a fundamental notion in the classical setting, it has been merely reflected so far in the probabilistic setting.
In this paper, we address the quantitative treatment of argumentation based on probabilistic notions of admissibility in a way that they form fully conservative extensions of classical notions.
In particular, our building blocks are not the beliefs regarding single arguments.
Instead we start from the fairly natural idea that whatever argumentation semantics is to be considered, semantics systematically induces constraints on the joint probability distribution on the sets of arguments.
In some cases there might be many such distributions, even infinitely many ones, in other cases there may be one or none.
Standard semantic notions are shown to induce such sets of constraints, and so do their probabilistic extensions.
This allows them to be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation.
We present a taxonomy of semantic notions, also in relation to published work, together with a running example illustrating our achievements.