Summary of Applying Attribution Explanations in Truth-discovery Quantitative Bipolar Argumentation Frameworks, by Xiang Yin et al.
Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks
by Xiang Yin, Nico Potyka, Francesca Toni
First submitted to arxiv on: 9 Sep 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 This paper explores the application of two explanation methods, Argument Attribution Explanations (AAEs) and Relation Attribution Explanations (RAEs), to Quantitative Bipolar Argumentation Frameworks (QBAFs) with complex cycles. The authors focus on Truth Discovery QBAFs, which evaluate the trustworthiness of sources and their claims. Both AAEs and RAEs are used to provide explanations for this task, demonstrating that they can offer interesting insights and surprising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we understand arguments when they’re connected in a special way. Researchers have been trying to figure out why some arguments are stronger than others by looking at the connections between them. Two ways people do this are by counting “scores” for each argument or connection, then using those scores to explain why certain arguments are strong or weak. This works pretty well for simple cases, but it’s not clear if it will work as well when there are lots of connections and things get complicated. The authors of this paper want to see how these methods do when they’re used on a special kind of “argument” that helps figure out whether sources (like websites) are trustworthy or not. |
