Learning Brave Assumption-Based Argumentation Frameworks via ASP

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Detalles Bibliográficos
Publicado en:arXiv.org (Nov 8, 2024), p. n/a
Autor principal: De Angelis, Emanuele
Otros Autores: Proietti, Maurizio, Toni, Francesca
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Cornell University Library, arXiv.org
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Acceso en línea:Citation/Abstract
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022 |a 2331-8422 
035 |a 3094933569 
045 0 |b d20241108 
100 1 |a De Angelis, Emanuele 
245 1 |a Learning Brave Assumption-Based Argumentation Frameworks via ASP 
260 |b Cornell University Library, arXiv.org  |c Nov 8, 2024 
513 |a Working Paper 
520 3 |a Assumption-based Argumentation (ABA) is advocated as a unifying formalism for various forms of non-monotonic reasoning, including logic programming. It allows capturing defeasible knowledge, subject to argumentative debate. While, in much existing work, ABA frameworks are given up-front, in this paper we focus on the problem of automating their learning from background knowledge and positive/negative examples. Unlike prior work, we newly frame the problem in terms of brave reasoning under stable extensions for ABA. We present a novel algorithm based on transformation rules (such as Rote Learning, Folding, Assumption Introduction and Fact Subsumption) and an implementation thereof that makes use of Answer Set Programming. Finally, we compare our technique to state-of-the-art ILP systems that learn defeasible knowledge. 
653 |a Algorithms 
653 |a Logic programming 
653 |a Declarative programming 
653 |a Machine learning 
653 |a Mathematical programming 
653 |a Reasoning 
700 1 |a Proietti, Maurizio 
700 1 |a Toni, Francesca 
773 0 |t arXiv.org  |g (Nov 8, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3094933569/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2408.10126