Unified Framework for Implementing Inaccurate Knowledge in Quantum Symbolic Artificial Intelligence Models

Eduardo Mosqueira-Rey, Samuel Magaz-Romero, Vicente Moret-Bonillo

2025

Abstract

Symbolic models of Artificial Intelligence are based on defining declarative knowledge that is connected through procedural knowledge forming symbolic graphs through which reasoning flows. Both declarative and procedural knowledge can be inaccurate, which has led to the definition of different models to represent this inaccuracy. Since the functioning of quantum computers is inherently probabilistic, it has been proposed to take advantage of this nature to implement inaccurate knowledge more effectively. In this paper, we present different models for implementing inaccurate knowledge in quantum computers and propose a unified framework to represent and implement the common features of all of them.

Paper Citation

in Harvard Style

Mosqueira-Rey E., Magaz-Romero S. and Moret-Bonillo V. (2025). Unified Framework for Implementing Inaccurate Knowledge in Quantum Symbolic Artificial Intelligence Models. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO; ISBN 978-989-758-737-5, SciTePress, pages 839-846. DOI: 10.5220/0013400200003890

in Bibtex Style

@conference{qaio25,
author={Eduardo Mosqueira-Rey and Samuel Magaz-Romero and Vicente Moret-Bonillo},
title={Unified Framework for Implementing Inaccurate Knowledge in Quantum Symbolic Artificial Intelligence Models},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO},
year={2025},
pages={839-846},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013400200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO
TI - Unified Framework for Implementing Inaccurate Knowledge in Quantum Symbolic Artificial Intelligence Models
SN - 978-989-758-737-5
AU - Mosqueira-Rey E.
AU - Magaz-Romero S.
AU - Moret-Bonillo V.
PY - 2025
SP - 839
EP - 846
DO - 10.5220/0013400200003890
PB - SciTePress