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Author: Alexander Sakharov

Affiliation: Synstretch, Framingham, MA, U.S.A.

Keyword(s): Knowledge Base, First-order Logic, Resolution, Backward Chaining, Neural-symbolic Computing, Tensorization.

Abstract: Inference methods for first-order logic are widely used in knowledge base engines. These methods are powerful but slow in general. Neural networks make it possible to rapidly approximate the truth values of ground atoms. A hybrid neural-symbolic inference method is proposed in this paper. It is a best-first search strategy for backward chaining. The strategy is based on neural approximations of the truth values of literals. This method is precise and the results are explainable. It speeds up inference by reducing backtracking.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Sakharov, A. (2021). A Best-first Backward-chaining Search Strategy based on Learned Predicate Representations. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8; ISSN 2184-433X, pages 982-989. DOI: 10.5220/0010299209820989

@conference{icaart21,
author={Alexander Sakharov.},
title={A Best-first Backward-chaining Search Strategy based on Learned Predicate Representations},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={982-989},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010299209820989},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Best-first Backward-chaining Search Strategy based on Learned Predicate Representations
SN - 978-989-758-484-8
IS - 2184-433X
AU - Sakharov, A.
PY - 2021
SP - 982
EP - 989
DO - 10.5220/0010299209820989