刘潇(Liu Xiao)

刘潇(Liu Xiao)

湘潭大学自动化与电子信息学院

About me

Hi, I am Xiao. I received my Ph.D. in Computer Science from Nanjing University in 2019 and 2023. Currently, I serve as a Lecturer in the School of Automation and Electronic Information at Xiangtan University. Our research interests primarily lie in Reinforcement Learning (RL) and Explainable Artificial Intelligence (XAI), with a focus on improving RL interpretability and designing scalable solutions for complex, real-world systems.

Courses

《人工智能微专业：Python编程与实践》, 《多智能体系统》, 《计算机操作系统》

Publications

  • Xizheng Qiao, Xinyi Peng, Kangrui Qi, Xiao Liu*. Enhancing der scheduling in active distribution networks: A symbolic deep reinforcement learning approach for improved interpretability and performance, 2025 IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering, 2025. (本科生一作)
  • Jing Shi, Xinyi Peng, Xizheng Qiao, Xiao Liu*. Balancing accuracy and explainability: An ensemble-kan model for patent grant prediction, 20th International Society of Scientometrics and Informetrics Conference, Yerevan, Armenia, 2025. (本科生二作)
  • Wubing Chen, Wenbin Li, Xiao Liu, Gao Yang*. Learning explicit credit assignment for cooperative multi-agent reinforcement learning via polarization policy gradient, proceedings of the aaai conference on artificial intelligence. Vol. 37 No. 10: Technical Tracks 10. 2023. https://doi.org/10.1609/aaai.v37i10.26364
  • Xiao Liu, Wenbin Li, Jing Huo, Lili Yao, Yang Gao*. Layerwise sparse coding for pruned deep neural networks with extreme compression ratio. Proceedings of the aaai conference on artificial intelligence. 34(04): 4900-4907. 2020. https://doi.org/10.1609/aaai.v34i04.5927
  • Yongxin Su, Mengyao Xu, Xiao Liu, Mao Tan*, Rui Wang, Chunhua Yang. Explainable reinforcement learning for enhancing personal thermal comfort and optimizing demand response in household multi-zone HVAC system. Science China. 2025. https://doi.org/10.1007/s11431-024-2895-7.
  • Mao Tan, Jie Zhao, Xiao Liu*, Yongxin Su, Ling Wang, Rui Wang, Zhuocen Dai, Federated reinforcement learning for smart and privacy-preserving energy management of residential microgrids clusters, Engineering Applications of Artificial Intelligence, Volume 139, Part B, 2025, 109579, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2024.109579.
  • Hui Li, Changhao Zhu*, Xiao Liu, Lijuan Li, Hongzhi Liu. Hybrid binarized neural network for high-accuracy classification of power quality disturbances. Electrical Engineering, 2024. https://doi.org/10.1007/s00202-024-02650-y
  • 曹宏业, 刘潇, 董绍康, 杨尚东, 霍静, 李文斌, 高阳*. 面向强化学习的可解释性研究综述. 计算机学报, Vol. 47 No. 8. 2024. http://cjc.ict.ac.cn/online/onlinepaper/chy-2024729180508.pdf
  • Wubing Chen, Shangdong Yang, Wenbin Li, Yujing Hu, Xiao Liu, Yang Gao*. Learning multi-intersection traffic signal control via coevolutionary multi-agent reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 2024. https://ieeexplore.ieee.org/abstract/document/10556581
  • Xiao Liu, Shuyang Liu, Bo An, Yang Gao*, Wenbin Li. Effective interpretable policy distillation via critical experience point identification. IEEE Intelligent Systems. Vol. 38, Issue: 5, Sept.-Oct. 2023. https://doi.org/10.1109/MIS.2023.3265868
  • 刘潇, 刘书洋, 庄韫恺, 高阳*. 强化学习可解释性基础问题探索和方法综述. 软件学报. 2023, 34(5): 2300-2316. https://doi.org/10.13328/j.cnki.jos.006485

Grants

  • 国家自然科学基金青年项目，62406272，多智能体强化学习的部署安全性验证方法，2025.1-2027.12，主持
  • 湖南省自然科学基金青年项目，2024JJ6438，深度强化学习可解释策略简化与规则提取研究，2025.1-2027.12，主持