Authors:
Chaim Schendowich
1
;
Eyal Ben Isaac
2
and
Rina Azoulay
1
Affiliations:
1
Dept. Computer Sciences, Lev Academic Center, Jerusalem, Israel
;
2
Dept. Data Mining, Lev Academic Center, Jerusalem, Israel
Keyword(s):
Sudoku, Deep Learning, One-Shot Prediction, Sequence Completion.
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.