@inproceedings{Tirsi:2023:10.1007/978-3-031-47546-7_1,
author = {Tirsi, C-G and Proietti, M and Toni, F},
doi = {10.1007/978-3-031-47546-7_1},
publisher = {Springer Nature},
title = {ABALearn: an automated logic-based learning system for ABA frameworks},
url = {http://dx.doi.org/10.1007/978-3-031-47546-7_1},
year = {2023}
}

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TY  - CPAPER
AB  - 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 logicprograms 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) andprovides insights into how such learners can be applied to make predictions.
AU  - Tirsi,C-G
AU  - Proietti,M
AU  - Toni,F
DO  - 10.1007/978-3-031-47546-7_1
PB  - Springer Nature
PY  - 2023///
SN  - 1687-7470
TI  - ABALearn: an automated logic-based learning system for ABA frameworks
UR  - http://dx.doi.org/10.1007/978-3-031-47546-7_1
UR  - http://hdl.handle.net/10044/1/106058
ER  - 

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