RLAR: A Reinforcement Learning Abductive Reasoner

Mostafa ElHayani

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Paper

RLAR: A Reinforcement Learning Abductive Reasoner

Topics: Explainable Artificial Intelligence; Knowledge Representation and Reasoning; Knowledge-Based Systems; Model-Based Reasoning; Neural Networks; State Space Search

In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 972-979, 2024 , Rome, Italy

Author: Mostafa ElHayani

Affiliation: Informatics, TUM, Munich, Germany

Keyword(s): Logic, Knowledge Representation and Reasoning, Abduction, Deep Reasoning, Reinforcement Learning, Machine Learning, Explainable Artificial Intelligence.

Abstract: Machine learning (ML) algorithms are the foundation of the modern AI environment. They are renowned for their capacity to solve complicated problems and generalize across a wide range of datasets. Nevertheless, a noteworthy disadvantage manifests itself as a lack of explainability. Symbolic AI is at the other extreme of the spectrum; in this case, every inference is a proof, allowing for transparency and traceability throughout the decision-making process. This paper proposes the Reinforcement Learning Abductive Reasoner (RLAR). A combination of modern and symbolic AI algorithms aimed to bridge the gap and utilize the best features of both methods. A case study has been chosen to test the implementation of the proposed reasoner. A knowledge-base (KB) vectorization step is implemented, and a Machine Learning model architecture is built to learn explanation inference. Furthermore, a simple abductive reasoner is also implemented to compare both approaches.

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Paper citation in several formats:

ElHayani, M. (2024). RLAR: A Reinforcement Learning Abductive Reasoner. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 972-979. DOI: 10.5220/0012425000003636

@conference{icaart24,
author={Mostafa ElHayani.},
title={RLAR: A Reinforcement Learning Abductive Reasoner},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={972-979},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012425000003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - RLAR: A Reinforcement Learning Abductive Reasoner
SN - 978-989-758-680-4
IS - 2184-433X
AU - ElHayani, M.
PY - 2024
SP - 972
EP - 979
DO - 10.5220/0012425000003636
PB - SciTePress