Summary of Instantiations and Computational Aspects Of Non-flat Assumption-based Argumentation, by Tuomo Lehtonen et al.
Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation
by Tuomo Lehtonen, Anna Rapberger, Francesca Toni, Markus Ulbricht, Johannes P. Wallner
First submitted to arxiv on: 17 Apr 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 addresses assumption-based argumentation (ABA) frameworks, which are typically considered flat. The researchers instead focus on an instantiation-based approach for reasoning in possibly non-flat ABA. They employ a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs), leveraging compilability theory to establish that constructed BAFs will be of exponential size. To reduce the number of arguments and computational cost, they propose three methods for identifying redundant arguments. Additionally, they identify fragments of ABA admitting poly-sized instantiations. Two algorithmic approaches are presented: one utilizing BAF instantiation and another working directly without constructing arguments. An empirical evaluation shows that the former outperforms the latter on many instances, reflecting the lower complexity of BAF reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to reason with assumptions in a more complex way than most tools do. Right now, most computational tools for assumption-based argumentation (ABA) focus on simple frameworks and ignore more general cases. The researchers instead take an approach that involves translating ABA into something called bipolar argumentation frameworks (BAFs). This allows them to use existing techniques to reason with assumptions in a way that’s efficient and effective. They also show how to identify and remove redundant arguments, which helps keep the number of steps needed to reach a conclusion lower. The paper presents two ways to do this reasoning: one that uses the BAF approach and another that doesn’t. It turns out that the first method is often better because it’s less complex. |
Keywords
» Artificial intelligence » Semantics » Translation
