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About this paper
Antonio Rago, Stylianos Loukas Vasileiou, Francesca Toni, Tran Cao Son, William Yeoh
This research introduces a modular approach to gradual semantics for argumentative statement graphs, explicitly designed to tolerate incomplete information by evaluating arguments with partially or entirely ungrounded premises. The method integrates existing abstract gradual semantics for quantitative bipolar argumentation frameworks, offering more robust argument evaluations in real-world scenarios where complete information is often unavailable.
Key insights
- The proposed DC semantics demonstrate robustness in handling incomplete information, maintaining a statement's intrinsic strength (Stability) and ensuring zero-strength arguments do not propagate effects (Neutrality), a key advantage over T-norm semantics.
- A comprehensive set of 13 properties, including novel ones tailored for incompleteness, was developed to evaluate gradual semantics in SGs, revealing nuanced behaviors regarding logical consistency and partial knowledge.
- Theoretical incompatibilities between desirable properties (e.g., Rewriting, Stability, and Top-Strength Premises) were identified, implying that no single semantics can satisfy all properties, necessitating context-dependent selection.
Summary by alphaXiv.
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