Learning Brave Assumption-Based Argumentation Frameworks via ASP

• URL: http://arxiv.org/abs/2408.10126v1
• Date: Mon, 19 Aug 2024 16:13:35 GMT
• Title: Learning Brave Assumption-Based Argumentation Frameworks via ASP
• Authors: Emanuele De Angelis, Maurizio Proietti, Francesca Toni,
• Abstract summary: Assumption-based Argumentation (ABA) is advocated as a unifying formalism for non-monotonic reasoning. In this paper we focus on the problem of automating their learning from background knowledge and positive/negative examples. 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.
• Score: 11.768331785549947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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