Summary of Learning Brave Assumption-based Argumentation Frameworks Via Asp, by Emanuele De Angelis (1) et al.
Learning Brave Assumption-Based Argumentation Frameworks via ASP
by Emanuele De Angelis, Maurizio Proietti, Francesca Toni
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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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 proposes a novel approach to learning Assumption-based Argumentation (ABA) frameworks from background knowledge and positive/negative examples. Unlike previous work, the authors frame this problem in terms of brave reasoning under stable extensions for ABA. They present a new algorithm that employs transformation rules, such as Rote Learning, Folding, Assumption Introduction, and Fact Subsumption, and demonstrate its implementation using Answer Set Programming. The authors also compare their technique to state-of-the-art ILP systems that learn defeasible knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers learn from examples and rules, called Assumption-based Argumentation (ABA). Usually, ABA frameworks are given beforehand, but this paper focuses on how to teach them to a computer using background information and positive or negative examples. The authors come up with a new algorithm that can do this and test it using a special programming language. They also compare their method to other top-performing systems. |
