A new Monte-Carlo-based technique for estimating the probability that a set of arguments is an extension is proposed.
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The performances of the proposed technique are thoroughly analyzed both experimentally and theoretically.
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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 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 by using a Monte-Carlo simulation technique, as computing 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 using much fewer samples than the state-of-the-art approach, resulting in a significantly more efficient estimation technique.