Summary of An Extension-based Approach For Computing and Verifying Preferences in Abstract Argumentation, by Quratul-ain Mahesar et al.
An Extension-based Approach for Computing and Verifying Preferences in Abstract Argumentation
by Quratul-ain Mahesar, Nir Oren, Wamberto W. Vasconcelos
First submitted to arxiv on: 26 Mar 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 proposed approach extends abstract argumentation systems by introducing preference-based frameworks that allow for the computation of justified arguments given a set of preferences. The inverse problem is considered, where an abstract framework and a set of justified arguments are used to compute possible preferences over arguments. A novel algorithm is presented for exhaustively computing and enumerating all possible sets of preferences in conflict-free argumentation frameworks. The algorithm is proven sound, complete, and terminating, with the complexity of computing sets of preferences being exponential in the number of arguments. An approximate approach and algorithm are also described to compute the preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new way of understanding abstract argumentation systems by introducing preferences that explain why certain arguments are accepted or rejected. The team developed a method to calculate these preferences given an abstract framework and some accepted arguments. They also created algorithms to check if these calculated preferences make sense and lead to the same accepted arguments. This work focuses on three specific types of semantics, known as grounded, preferred, and stable. The researchers found that their approach can get stuck in very long computation times when dealing with large numbers of arguments, so they developed a simpler way to estimate the preferences. |
Keywords
» Artificial intelligence » Semantics
