Advanced Machine Learning Techniques in Gomoku: Strategy,Implementation, and Analysis
DOI:
https://doi.org/10.61173/4sda3g67Keywords:
Gomoku, artificial intelligence, neural networks, strategic decision-makingAbstract
The strategic board game Gomoku has become a compelling domain for artificial intelligence (AI) research, particularly in developing and applying machine learning techniques. This paper comprehensively analyzes advanced machine learning strategies in Gomoku, focusing on logistic regression for board evaluation, neural networks for pattern recognition, and reinforcement learning for strategic gameplay. We discuss integrating these techniques in creating a sophisticated AI capable of high-level play and adaptability. Through this exploration, we highlight the potential of AI in strategic decision-making and its broader applications beyond board games.
References
[1] Alpaydin, E. (2014). Introduction to Machine Learning. MIT Press.
[2] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
[3] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
[4] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.
[5] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
[6] Lai, S.-C. (2001). Renju: For Beginners to Advanced Players. Sterling Publishing.
[7] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Dean&Francis Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
[8] Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
[9] Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
[10] Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
[11] Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354- 359.
[12] Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., ... & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144.
[13] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929-1958.
[14] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
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