Abnormal Predicates: Learning Categorical Defaults from Probabilistic Rules

Rose Azad Khan, Vaishak Belle

2025

Abstract

Learning defaults is a longstanding goal in the field of knowledge representation and reasoning. We provide a novel method for learning defaults by way of introducing a new predicate: the abnormal predicate, which explicitly covers all the exceptions to a rule, thus forming a default theory. Our proposed method for learning defaults is sound and complete for all rule-exceptions, and can be extended for use on other frameworks.

Paper Citation

in Harvard Style

Khan R. and Belle V. (2025). Abnormal Predicates: Learning Categorical Defaults from Probabilistic Rules. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 892-900. DOI: 10.5220/0013229000003890

in Bibtex Style

@conference{icaart25,
author={Rose Khan and Vaishak Belle},
title={Abnormal Predicates: Learning Categorical Defaults from Probabilistic Rules},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={892-900},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013229000003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Abnormal Predicates: Learning Categorical Defaults from Probabilistic Rules
SN - 978-989-758-737-5
AU - Khan R.
AU - Belle V.
PY - 2025
SP - 892
EP - 900
DO - 10.5220/0013229000003890
PB - SciTePress