Summary of Ce-qarg: Counterfactual Explanations For Quantitative Bipolar Argumentation Frameworks (technical Report), by Xiang Yin et al.
CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report)
by Xiang Yin, Nico Potyka, Francesca Toni
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 a novel approach to understanding and manipulating the strength of arguments in Quantitative Bipolar Argumentation Frameworks (QBAFs). Existing methods focus on explaining the importance of individual arguments, but neglect how to change the overall argument strength. The authors introduce counterfactual explanations for QBAFs, which identify valid and cost-effective ways to update the argument strength. The proposed algorithm, CE-QArg, consists of two core modules: polarity and priority, which determine the updating direction and magnitude for each argument. The paper discusses formal properties of the counterfactual explanations and evaluates them on randomly generated QBAFs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make arguments stronger or weaker in a special kind of argument framework called Quantitative Bipolar Argumentation Frameworks (QBAFs). Right now, most researchers are good at explaining why an argument is strong or weak, but they don’t know how to change it. The authors come up with a new way to explain and improve the strength of QBAF arguments by finding valid and cost-effective ways to update them. They use two important ideas: polarity and priority, which help decide what changes to make to each argument. |
