AAI_2025_Capstone_Chronicles_Combined

Evaluating Deep Learning Model Convergence in Chess via Nash Equilibria

18

References Balduzzi, D., Tuyls, K., Perolat, J., & Graepel, T. (2020). Re-evaluating evaluation. arXiv preprint arXiv:1806.02643. https://arxiv.org/abs/1806.02643

Elo, A. E. (1978). “The rating of chessplayers, past and present”. Arco Publishing. Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2019). Squeeze-and-excitation networks. arXiv.org. https://arxiv.org/abs/1709.01507

Kemker, R., McClure, M., Abitino, A., Hayes, T. L., & Kanan, C. (2018). Measuring catastrophic forgetting in neural networks. Proceedings of the AAAI Conference on Artificial Intelligence , 32(1). https://doi.org/10.1609/aaai.v32i1.11773

Monroe, D., Chalmers, P. (2024). Mastering Chess with a Transformer Model. arXiv preprint arXiv:2409.12272 . https://arxiv.org/abs/2409.12272

Michie, D. (2000). David Champernowne (1912–2000). ICGA Journal , 23(4), 233–235. https://doi.org/10.3233/ICG-2000-23419

Ortiz, L.E., Schapire, R.E. & Kakade, S.M.. (2007). Maximum Entropy Correlated Equilibria. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:347-354 Available from https://proceedings.mlr.press/v2/ortiz07a.html .

92