Generation of Sokoban Stages using Recurrent Neural Networks
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 3
Abstract
Puzzles and board games represent several important classes of AI problems, but also represent difficult complexity classes. In this paper, we propose a deep learning based alternative to train a neural network model to find solution states of the popular puzzle game Sokoban. The network trains against a classical solver that uses theorem proving as the oracle of valid and invalid games states, in a setup that is similar to the popular adversarial training framework. Using our approach, we have been able to verify the validity of a Sokoban puzzle up to an accuracy of 99% on the test set. We have also been able to train our network to generate the next possible state of the puzzle board up to an accuracy of 99% on the validation set. We hope that through this approach, a trained neural network will be able to replace human experts and classical rule-based AI in generating new instances and solutions for such games.
Authors and Affiliations
Muhammad Suleman, Farrukh Hasan Syed, Tahir Q. Syed, Saqib Arfeen, Sadaf I. Behlim, Behroz Mirza
Keywords
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- EP ID EP251137
- DOI 10.14569/IJACSA.2017.080364
- Views 112
- Downloads 0
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