Uncertainty in Quantitative Bipolar Argumentation Frameworks

Online deliberation platforms allow people to exchange their opinions around a specified issue and to vote on these opinions in order to reach a collective decision. Argumentation allows to structure and analyse user input for these platforms. A debate can be represented by a quantitative bipolar argumentation framework where votes on each argument of the debate are aggregated into an initial weight. One of the main challenges these platforms face is sparse voting i. e. participants vote on a few number of arguments, leading to an imbalance of the number of votes between the arguments. In this paper, we propose a methodology that handles sparse voting in online debates, by introducing imprecise quantitative bipolar argumentation frameworks that incorporate uncertainty on the initial weights. Specifically, we leverage votes on arguments to initialize weight intervals that represent the uncertainty on the initial weights, using the imprecise Dirichlet model. We use four state-of-the-art bipolar gradual semantics to generate a final acceptability interval on each argument and we introduce several properties to study the effect of these semantics on the uncertainty on each argument’s final evaluation. Our methodology allows for a more robust representation of argument strength in the presence of limited data.

Authors

• Jordan Thieyre
• Caren Al Anaissy
• Aurélie Beynier
• Sébastien Destercke
• Nicolas Maudet
• Srdjan Vesic

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