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Pawar, Ambar Balkrishna (2023) An Empirical Study of AttLSTM Neural Networks for Chess Move Prediction. Masters thesis, Dublin, National College of Ireland.
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Abstract
This research present a comprehensive exploration of advanced chess move prediction using deep learning techniques. Leveraging various Python libraries, including TensorFlow and Keras, the research meticulously construct and fine-tune an Attention-enhanced LSTM model for predicting subsequent chess moves based on historical game data. Through thorough evaluation, model showcases commendable accuracy in move prediction, achieving 87% on the test dataset. Employing graphical simulations, the research visually depict the capabilities of the model in generating chessboard states and predicting moves. Furthermore, research demonstrate the model’s strategic capabilities by engaging it in chess matches against the renowned Stockfish engine. Impressively, model manages to secure 4 draws against Stockfish, a remarkable feat considering its status as one of the most powerful chess engines. This study also encompasses insights into the dataset utilized, which spans a diverse collection of chess games. Overall, this research contributes to the advancement of chess move prediction methodologies and underscores the potential of deep learning in complex board games.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Siddig, Abubakr UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GV Recreation Leisure > Games and Amusements Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Tamara Malone |
| Date Deposited: | 28 Dec 2024 14:19 |
| Last Modified: | 28 Dec 2024 14:19 |
| URI: | https://norma.ncirl.ie/id/eprint/7247 |
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