Summary of Impact Measures For Gradual Argumentation Semantics, by Caren Al Anaissy et al.
Impact Measures for Gradual Argumentation Semantics
by Caren Al Anaissy, Jérôme Delobelle, Srdjan Vesic, Bruno Yun
First submitted to arxiv on: 11 Jul 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 The paper proposes refinements to existing impact measures for argumentation models, which assess the influence of other arguments on an individual argument’s score. The authors introduce a new measure rooted in Shapley values and evaluate its performance alongside an existing measure from Delobelle and Villata using several well-known gradual semantics. This analysis provides insights into the functionality and desirability of these impact measures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to understand how different ideas affect each other when we’re trying to make a decision. It’s like figuring out how much someone else’s opinion changes our own. The researchers improve an old way of doing this and create a new one based on something called Shapley values. They test both methods using different rules for how arguments work together. This helps us understand which method is better and why. |
Keywords
» Artificial intelligence » Semantics
