Learning Brave Assumption-Based Argumentation Frameworks via ASP
This work addresses the challenge of automating ABA framework learning for non-monotonic reasoning, which is incremental as it builds on prior methods by introducing a brave reasoning approach.
The paper tackles the problem of automating the learning of Assumption-based Argumentation (ABA) frameworks from background knowledge and examples, framing it as brave reasoning under stable extensions, and presents a novel algorithm implemented with Answer Set Programming that is compared to state-of-the-art ILP systems.
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.