ABALearn: an automated logic-based learning system for ABA frameworks
File(s)
Accepted version
Author(s)
Tirsi, Cristina-Gabriela
Proietti, Maurizio
Toni, Francesca
Type
Conference Paper
Abstract
We introduce ABALearn, an automated algorithm that learns Assumption-Based Argumentation (ABA) frameworks from training data consisting of positive and negative examples, and a given background knowledge. ABALearn’s ability to generate comprehensible rules for decision-making promotes transparency and interpretability, addressing the challenges associated with the black-box nature of traditional machine learning models. This implementation is based on the strategy proposed in a previous work. The resulting ABA frameworks can be mapped onto logic
programs with negation as failure. The main advantage of this algorithm is that it requires minimal information about the learning problem and it is also capable of learning circular debates. Our results show that this approach is competitive with state-of-the-art alternatives, demonstrat-
ing its potential to be used in real-world applications. Overall, this work contributes to the development of automated learning techniques for argumentation frameworks in the context of Explainable AI (XAI) and
provides insights into how such learners can be applied to make predictions.
programs with negation as failure. The main advantage of this algorithm is that it requires minimal information about the learning problem and it is also capable of learning circular debates. Our results show that this approach is competitive with state-of-the-art alternatives, demonstrat-
ing its potential to be used in real-world applications. Overall, this work contributes to the development of automated learning techniques for argumentation frameworks in the context of Explainable AI (XAI) and
provides insights into how such learners can be applied to make predictions.
Date Issued
2023-11-02
Date Acceptance
2023-08-01
Citation
Advances in Artificial Intelligence, 2023, 14318
ISBN
9783031475450
ISSN
1687-7470
Publisher
Springer Nature
Journal / Book Title
Advances in Artificial Intelligence
Volume
14318
Copyright Statement
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://link.springer.com/chapter/10.1007/978-3-031-47546-7_1
Source
AIxIA 2023
Publication Status
Published
Start Date
2023-11-06
Finish Date
2023-11-09
Coverage Spatial
Rome, Italy