Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches
Sylwia Polberg, Anthony Hunter

TL;DR
This paper empirically evaluates argumentation frameworks, highlighting the importance of bipolar and probabilistic methods for more accurately modeling human beliefs and opinions in dialogical contexts.
Contribution
It provides experimental evidence supporting the need for bipolar and probabilistic approaches over traditional attack-only frameworks in argumentation.
Findings
Probabilistic epistemic approach better models individual beliefs.
Bipolar frameworks capture positive and negative relations effectively.
Experimental results favor more flexible argumentation models.
Abstract
In dialogical argumentation it is often assumed that the involved parties always correctly identify the intended statements posited by each other, realize all of the associated relations, conform to the three acceptability states (accepted, rejected, undecided), adjust their views when new and correct information comes in, and that a framework handling only attack relations is sufficient to represent their opinions. Although it is natural to make these assumptions as a starting point for further research, removing them or even acknowledging that such removal should happen is more challenging for some of these concepts than for others. Probabilistic argumentation is one of the approaches that can be harnessed for more accurate user modelling. The epistemic approach allows us to represent how much a given argument is believed by a given person, offering us the possibility to express more…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Topic Modeling · Speech and dialogue systems
