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Learning Brave Assumption-Based Argumentation Frameworks via ASP

Gardado en:
Publicado en:arXiv.org (Nov 8, 2024), p. n/a
Autor Principal: De Angelis, Emanuele
Outros autores: Proietti, Maurizio, Toni, Francesca
Publicado:
Cornell University Library, arXiv.org
Materias:
Algorithms
Logic programming
Declarative programming
Machine learning
Mathematical programming
Reasoning
Acceso en liña:Citation/Abstract
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Resumo: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.
ISSN:2331-8422
Fonte:Engineering Database