Publications

Y. Li*, Z. Wang*, Q. Zhang, B. Yuan and J. Liu, "Measuring Diversity of Game Scenarios," IEEE Transactions on Games (ToG), doi: doi: 10.1109/TG.2025.3543135.

Q. Zhang, Z. Wang, Y. Li, K. Zhang, B. Yuan and J. Liu, "Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning," IEEE Transactions on Artificial Intelligence (TAI), doi: 10.1109/TAI.2024.3489534.

Z. Wang*, Y. Li*, H. Du, J. Liu and G. N. Yannakakis, "Fun as moderate divergence: Evaluating experience-driven PCG via RL," IEEE Transactions on Games (ToG), doi: 10.1109/TG.2024.3456101.

C. Hu, Y. Zhao, Z. Wang, H. Du and J. Liu, "Games for artificial intelligence research: A review and perspectives," IEEE Transactions on Artificial Intelligence (TAI), doi: 10.1109/TAI.2024.3410935.

B. Yuan, S. Gui, Q. Zhang, Z. Wang, J, Wen, B. Mao, J. Liu and X. Yao, "FairerML: An extensible platform for analysing, visualising, and mitigating biases in machine learning," IEEE Computational Intelligence Magazine (CIM), vol. 19, no. 2, pp. 129-141, 2024.

Z. Wang, C. Hu, J. Liu and X. Yao, "Negatively correlated ensemble reinforcement learning for online diverse game level generation," in International Conference on Learning Representations (ICLR), 2024. (pdf)

Z. Wang, T. Shu and J. Liu, "State space closure: Revisiting endless online level generation via reinforcement learning, IEEE Transactions on Games (ToG), vol. 16, no. 2, pp. 489-492, 2024. (Letter paper) (pdf)

C. Hu, Z. Wang, T. Shu, H. Tong, J. Togelius, X. Yao and J. Liu, "Reinforcement learning with dual-observation for general video game playing," IEEE Transactions on Games (ToG), vol. 15, no. 2, pp. 202-216, 2023. (pdf)

Z. Wang, J. Liu and G. N. Yannakakis, "The fun facets of Mario: Multifaceted experience-driven PCG via reinforcement learning," in International Conference on Foundation of Digital Games (FDG), ACM, 2022, pp. 1-8. (pdf)

Z. Wang and J. Liu, "Online game level generation from music," in Conference on Games (CoG), IEEE, 2022, pp. 119-126. (pdf)

Z. Wang, J. Liu and G. N. Yannakakis, "Keiki: Towards realistic Danmaku generation via sequential GANs,” in Conference on Games (CoG), IEEE, 2021, pp. 1-4. (Short paper) (pdf)

T. Shu*, Z. Wang*, J. Liu and X. Yao, "A novel CNet-assisted evolutionary level repairer and its applications to Super Mario Bros," in Congress on Evolutionary Computation (CEC), IEEE, 2020, pp. 1-10. (pdf)