KR2026Proceedings of the 23rd International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning

Lisbon, Portugal. July 20-23, 2026.

Edited by
  1. Renata Wassermann
  2. Marie-Laure Mugnier
  3. Franz Baader

ISSN: 2334-1033
ISBN: 978-1-956792-18-8

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Published by

Copyright © 2026 International Joint Conferences on Artificial Intelligence Organization

X-ABALearn: Argumentative Learning with Semantics à la Carte

  1. Emanuele De Angelis(CNR-IASI, Rome, Italy)
  2. Maurizio Proietti(CNR-IASI, Rome, Italy)
  3. Francesca Toni(Imperial, London, UK)
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BibTeX
https://doi.org/10.24963/kr.2026/85

Keywords

  1. null-Assumption-based Argumentation
  2. null-Symbolic learning
  3. null-Explainable AI
  4. null-Answer Set Programming
  5. null-Semantics

Abstract

ABA Learning is a recent approach for obtaining Assumption-based Argumentation (ABA) frameworks by reasoning with transformation rules from background knowledge and positive/negative examples of concepts of interest.

ABA Learning relies on credulous reasoning under a specific semantic notion of extensions for ABA, namely that of stable extensions.

In this paper, we newly frame the problem in terms of credulous reasoning under any semantic notion of extensions for ABA.

Focusing on admissible, complete, grounded, preferred as well as stable extensions, we present X-ABALearn, a novel parametric algorithm (with X any ABA semantics) based on variants of the transformation

rules of ABA Learning and an implementation thereof in Answer Set Programming.

Finally, we explore the use of (our implementation of) X-ABALearn on several learning problems, including tabular data and beyond.