On efficiently estimating the probability of extensions in abstract argumentation frameworks,☆☆

https://doi.org/10.1016/j.ijar.2015.11.009Get rights and content
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Highlights

  • A new Monte-Carlo-based technique for estimating the probability that a set of arguments is an extension is proposed.

  • The performances of the proposed technique are thoroughly analyzed both experimentally and theoretically.

  • The proposed technique outperforms the state of the art.

Abstract

Probabilistic abstract argumentation is an extension of Dung's abstract argumentation framework with probability theory. In this setting, we address the problem of computing the probability Prsem(S) that a set S of arguments is an extension according to a semantics sem. We focus on four popular semantics (i.e., complete, grounded, preferred and ideal-set) for which the state-of-the-art approach is that of estimating Prsem(S) by using a Monte-Carlo simulation technique, as computing Prsem(S) has been proved to be intractable. In this paper, we propose a new Monte-Carlo simulation approach which exploits some properties of the above-mentioned semantics for estimating Prsem(S) using much fewer samples than the state-of-the-art approach, resulting in a significantly more efficient estimation technique.

Keywords

Probabilistic reasoning
Argumentation systems
Monte-Carlo simulation

Cited by (0)

An abridged version of this paper appeared in [1].

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The first and the second author were partially supported by the project PON01_01286 – eJRM (electronic Justice Relationship Management).