Multiverse: A Deep Learning 4X4 Sudoku Solver

Chaim Schendowich, Eyal Ben Isaac, Rina Azoulay

2024

Abstract

This paper presents a novel deep learning-based approach to solving 4x4 Sudoku puzzles, by viewing Sudoku as a complex multi-level sequence completion problem. It introduces a neural network model, termed as ”Multiverse”, which comprises multiple parallel computational units, or ”verses”. Each unit is designed for sequence completion based on Long Short-Term Memory (LSTM) modules. The paper’s novel perspective views Sudoku as a sequence completion task rather than a pure constraint satisfaction problem. The study generated its own dataset for 4x4 Sudoku puzzles and proposed variants of the Multiverse model for comparison and validation purposes. Comparative analysis shows that the proposed model is competitive with, and potentially superior to, state-of-the-art models. Notably, the proposed model was able to solve the puzzles in a single prediction, which offers promising avenues for further research on larger, more complex Sudoku puzzles.

Paper Citation

in Harvard Style

Schendowich C., Ben Isaac E. and Azoulay R. (2024). Multiverse: A Deep Learning 4X4 Sudoku Solver. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 15-22. DOI: 10.5220/0012232500003636

in Bibtex Style

@conference{icaart24,
author={Chaim Schendowich and Eyal Ben Isaac and Rina Azoulay},
title={Multiverse: A Deep Learning 4X4 Sudoku Solver},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={15-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012232500003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Multiverse: A Deep Learning 4X4 Sudoku Solver
SN - 978-989-758-680-4
AU - Schendowich C.
AU - Ben Isaac E.
AU - Azoulay R.
PY - 2024
SP - 15
EP - 22
DO - 10.5220/0012232500003636
PB - SciTePress