Summary of A Methodology For Incompleteness-tolerant and Modular Gradual Semantics For Argumentative Statement Graphs, by Antonio Rago et al.
A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
by Antonio Rago, Stylianos Loukas Vasileiou, Francesca Toni, Tran Cao Son, William Yeoh
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel methodology is proposed for obtaining gradual semantics (GS) for statement graphs, a type of structured argumentation framework. This approach accommodates incomplete information and can leverage on any GS for quantitative bipolar argumentation frameworks (QBAFs). The paper defines a set of novel properties for the GS and studies their suitability alongside existing properties for two instantiations of the GS, demonstrating advantages over existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how people argue is presented in this paper. It’s called gradual semantics, which helps us figure out how to make sense of incomplete information. This approach can be used with other methods that help machines understand arguments and make decisions. The authors come up with some new ideas for understanding how arguments work and test them on two different scenarios. |
Keywords
» Artificial intelligence » Semantics
