Ancillary mechanism for autonomous decision-making process in asymmetric confrontation: a view from Gomoku

References

• Banerjee, B., & Peng, J. (2012). Strategic best-response learning in multiagent systems. Journal of Experimental & Theoretical Artificial Intelligence, 24(2), 139–160. https://doi.org/10.1080/13623079.2011.571819
   Web of Science ®Google Scholar
• Barrett, J. A., Cochran, C. T., Huttegger, S., & Fujiwara, N. (2017). Hybrid learning in signalling games. Journal of Experimental & Theoretical Artificial Intelligence, 29(5), 1119–1127. https://doi.org/10.1080/0952813X.2017.1309693
   Web of Science ®Google Scholar
• Ben-Assayag, S., & El-Yaniv, R. (2021). Train on small, play the large: Scaling up board games with alphazero and gnn. arXiv preprint arXiv:2107.08387.
   Google Scholar
• Bhattacharyya, R., & Hazarika, S. (2020). A knowledge-driven layered inverse reinforcement learning approach for recognizing human intents. Journal of Experimental & Theoretical Artificial Intelligence, 32(6), 1015–1044. https://doi.org/10.1080/0952813X.2020.1718773
   Web of Science ®Google Scholar
• Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., & Colton, S. (2012). A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 1–43. https://doi.org/10.1109/TCIAIG.2012.2186810
   Web of Science ®Google Scholar
• Coulom, R. (2006). Efficient selectivity and backup operators in monte-carlo tree search. In International conference on computers and games, 72–83. Berlin, Heidelberg: Springer.
   Google Scholar
• De Winter, G. (2018). Ai personalities: Clues from animal research. Journal of Experimental & Theoretical Artificial Intelligence, 30(5), 547–559 https://doi.org/10.1080/0952813X.2018.1430861.
   Web of Science ®Google Scholar
• Esposito, G., & Mart´ın, M. (2016). Approximate policy iteration using regularised bellman residuals minimisation. Journal of Experimental & Theoretical Artificial Intelligence, 28(1–2), 351–367. https://doi.org/10.1080/0952813X.2015.1024494
   Web of Science ®Google Scholar
• Garc´ıa, J., Majadas, R., & Fernandez, F. (2020). Learning adversarial attack policies through multi-objective reinforcement learning. Engineering Applications of Artificial Intelligence, 96 , 104021. https://doi.org/10.1016/j.engappai.2020.104021
   Web of Science ®Google Scholar
• Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (vol. 1, no. 2). MIT Press. Retrieved December, 21:2020.
   Google Scholar
• Guarini, M. (2009). Computational theories of mind, and fodor’s analysis of neural network behaviour. Journal of Experimental & Theoretical Artificial Intelligence, 21(2), 137–153. https://doi.org/10.1080/09528130902723674
   Web of Science ®Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016a). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, Nevada, USA, 770–778.
   Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016b). Identity mappings in deep residual networks. In European conference on computer vision, Amsterdam, The Netherlands, 630–645. : Springer.
   Google Scholar
• Karigiannis, J. N., & Tzafestas, C. S. (2016). Model-free learning on robot kinematic chains using a nested multi-agent topology. Journal of Experimental & Theoretical Artificial Intelligence, 28(6), 913–954. https://doi.org/10.1080/0952813X.2015.1042923
   Web of Science ®Google Scholar
• Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98–113. https://doi.org/10.1109/72.554195
   PubMed Web of Science ®Google Scholar
• Li, Y., Fang, Y., & Akhtar, Z. (2020). Accelerating deep reinforcement learning model for game strategy. Neurocomputing, 408 , 157–168 . https://doi.org/10.1016/j.neucom.2019.06.110
   Web of Science ®Google Scholar
• Li, X., He, S., Wu, L., Chen, D., & Zhao, Y. (2019). A game model for gomoku based on deep learning and monte carlo tree search. In Chinese Intelligent Automation Conference, Singapore, 88–97. Springer.
   Google Scholar
• Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., & Kavukcuoglu, K. (2016). Asynchronous methods for deep reinforcement learning. In International conference on machine learning, New York, New York, USA, 1928–1937. PMLR.
   Google Scholar
• Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
   Google Scholar
• Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
   PubMed Web of Science ®Google Scholar
• Perez, D., Samothrakis, S., & Lucas, S. (2014). Knowledge-based fast evolutionary mcts for general video game playing. In 2014 IEEE Conference on Computational Intelligence and Games, Dortmund, Germany, 1–8. IEEE.
   Google Scholar
• Rakhmatulin, I. (2021). Neural network for automatic farm control. Journal of Experimental & Theoretical Artificial Intelligence, 33(1), 147–160. https://doi.org/10.1080/0952813X.2020.1725653
   Web of Science ®Google Scholar
• Ranjitha, M., Nathan, K., & Joseph, L. (2020). Artificial intelligence algorithms and techniques in the computation of player-adaptive games. In Journal of Physics: Conference Series, Bangalore, Karnataka, India, volume 1427, 012006. IOP Publishing.
   Google Scholar
• Santos, J. M., & Touzet, C. (1999). Dynamic update of the reinforcement function during learning. Connection Science, 11(3–4), 267–289. https://doi.org/10.1080/095400999116250
   Web of Science ®Google Scholar
• Sarma, S. K. (2021). Hybrid optimised deep learning-deep belief network for attack detection in the internet of things. Journal of Experimental & Theoretical Artificial Intelligence, 1–30. https://doi.org/10.1080/0952813X.2021.1924868
   Web of Science ®Google Scholar
• Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks: The Official Journal of the International Neural Network Society, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
   PubMed Web of Science ®Google Scholar
• Shahamiri, S. R. (2021). Neural network-based multi-view enhanced multi-learner active learning: Theory and experiments. Journal of Experimental & Theoretical Artificial Intelligence, 1–21. https://doi.org/10.1080/0952813X.2021.1948921
   Web of Science ®Google Scholar
• Shao, K., Zhao, D., Tang, Z., & Zhu, Y. (2016). Move prediction in gomoku using deep learning. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China, 292–297. IEEE.
   Google Scholar
• Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
   PubMed Web of Science ®Google Scholar
• Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., Hassabis, D. (2017a). Mastering chess and shogi by self-play with a general reinforcement learning algorithm 2017a . arXiv preprint arXiv:1712.01815.
   Google Scholar
• Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2017b). Mastering the game of go without human knowledge. nature, 550(7676), 354–359. https://doi.org/10.1038/nature24270
   PubMed Web of Science ®Google Scholar
• Soemers, D. J., Mella, V., Piette, E., Stephenson, M., Browne, C., & Teytaud, O. (2021). Transfer of fully convolutional policy-value networks between games and game variants. arXiv preprint arXiv:2102.12375.
   Google Scholar
• Srivastava, A., & Yang, L. (2020). A reimplementation of alphago-zero on a game of gomoku.
   Google Scholar
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
   Google Scholar
• van Eck, N. J., & van Wezel, M. (2008). Application of reinforcement learning to the game of othello. Computers & Operations Research, 35(6), 1999–2017. https://doi.org/10.1016/j.cor.2006.10.004
   Web of Science ®Google Scholar
• Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J. P., Jaderberg, M., … Silver, D. (2019). Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782), 350–354. https://doi.org/10.1038/s41586-019-1724-z
   PubMed Web of Science ®Google Scholar
• Von Stackelberg, H. (2010). Market structure and equilibrium. Springer Science & Business Media.
   Google Scholar
• Vylala, A., & Plakkottu Radhakrishnan, B. (2020). Spectral feature and optimization-based actor-critic neural network for arrhythmia classification using ECG signal. Journal of Experimental & Theoretical Artificial Intelligence, 32(3), 409–435. https://doi.org/10.1080/0952813X.2019.1652355
   Web of Science ®Google Scholar
• Yan, P., & Feng, Y. (2018a). A hybrid gomoku deep learning artificial intelligence. In Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference, New York, NY, USA, Association for Computing Machinery, 48–52.
   Google Scholar
• Yan, P., & Feng, Y. (2018b). Using convolution and deep learning in gomoku game artificial intelligence. Modern Physics Letters A, 28(3), 1850011 https://doi.org/10.1142/S0129626418500111.
   Google Scholar
• Zhang, R. (2016). Convolutional and recurrent neural network for gomoku.
   Google Scholar