Abstract
Learning Automata (LA) in combination with Cellular Automata (CA) have been proven to be viable candidates as a mean to deal with problems of high complexity. Their ability to learn and adapt combined with their inherit parallelism can speed-up the solution process of various problems, including complex logic puzzles. A well-known logic puzzle is the Sudoku, which is a combinatorial optimization problem of increased difficulty and complexity. In this work, the representation of a Sudoku puzzle as a Irregular Learning Cellular Automaton (ILCA) has been explored, incorporating the necessary rules of a reward and penalty algorithm as a resolution process. The results prove the successful operation of the proposed algorithm, highlighting the concurrent and learning capabilities of the ILCA structure.
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Acknowledgments
The work of Rafailia-Eleni Karamani was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number: 1333).
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Chatzinikolaou, T.P., Karamani, RE., Sirakoulis, G.C. (2022). Irregular Learning Cellular Automata for the Resolution of Complex Logic Puzzles. In: Chopard, B., Bandini, S., Dennunzio, A., Arabi Haddad, M. (eds) Cellular Automata. ACRI 2022. Lecture Notes in Computer Science, vol 13402. Springer, Cham. https://doi.org/10.1007/978-3-031-14926-9_32
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