个人简介 :

李凯，中国科学院自动化研究所副研究员，博士毕业于中国科学院自动化研究所模式识别国家重点实验室，研究领域为智能博弈、强化学习、游戏AI等，2020 CCF-腾讯犀牛鸟科研基金优秀奖获得者，中国科学院大学人工智能学院及南京学院《计算博弈原理与应用》课程主讲教师，负责国家自然科学基金、中科院先导专项子课题、科技创新2030-“新一代人工智能”重大项目子课题等多项重要科研任务，在包括CCF-A类国际期刊会议（如NeurIPS、ICML、ICLR、AAAI、IJCAI、AAMAS等）上发表学术论文20余篇，获人工智能顶级会议AAAI 2022卓越论文奖，申请/授权国家发明专利20余项，获多次国际竞赛冠军，在不完美信息博弈、深度强化学习、多智能体系统、智能视觉感知等方面具有丰富的研究与应用经验，近年来围绕大规模复杂博弈环境下的智能决策问题，研发了一系列高水平的决策AI，并构建了学界首个大规模不完美信息博弈研究平台OpenHoldem。

发表论文 :

• Not All Tasks Are Equally Difficult: Multi-Task Deep Reinforcement Learning with Dynamic Depth RoutingJinmin He, Kai Li, Yifan Zang, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng.AAAI Conference on Artificial Intelligence (AAAI), 2024.
• Automatic Grouping for Efficient Cooperative Multi-Agent Reinforcement LearningYifan Zang, Jinmin He, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng.Neural Information Processing Systems (NeurIPS), 2023.
• OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game ResearchKai Li, Hang Xu, Enmin Zhao, Zhe Wu, Junliang Xing.IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.
• Sample Efficient Reinforcement Learning Using Graph-Based Memory ReconstructionYongxin Kang, Enmin Zhao, Yifan Zang, Lijuan Li, Kai Li, Pin Tao, Junliang Xing.IEEE Transactions on Artificial Intelligence (TAI), 2023.
• Sequential Cooperative Multi-Agent Reinforcement LearningYifan Zang, Jinmin He, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing.International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023.
• Greedy when Sure and Conservative when Uncertain about the OpponentsHaobo Fu, Ye Tian, Hongxiang Yu, Weiming Liu, Shuang Wu, Jiechao Xiong, Ying Wen, Kai Li, Junliang Xing, Qiang Fu, Wei YangInternational Conference on Machine Learning (ICML), 2022, Spotlight.
• Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information GameHaobo Fu, Weiming Liu, Shuang Wu, Yijia Wang, Tao Yang, Kai Li, Junliang Xing, Bin Li, Bo Ma, Qiang Fu, Yang Wei.International Conference on Learning Representations (ICLR), 2022.
• AutoCFR: Learning to Design Counterfactual Regret Minimization AlgorithmsHang Xu, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing.AAAI Conference on Artificial Intelligence (AAAI), 2022, Oral.
• AlphaHoldem: High-Performance Artificial Intelligence for Heads-Up No-Limit Poker via End-to-End Reinforcement LearningEnmin Zhao, Renye Yan, Jinqiu Li, Kai Li, Junliang Xing.AAAI Conference on Artificial Intelligence (AAAI), 2022. Distinguished Paper Award!
• Exploration via State Influence ModelingYongxin Kang, Enmin Zhao, Kai Li, Junliang Xing.AAAI Conference on Artificial Intelligence (AAAI), 2021.
• Potential Driven Reinforcement Learning for Hard Exploration TasksEnmin Zhao, Shihong Deng, Yifan Zang, Yongxin Kang, Kai Li, Junliang Xing.International Joint Conference on Artificial Intelligence (IJCAI), 2020.
• Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age EstimationKai Li, Junliang Xing, Chi Su, Weiming Hu, Yundong Zhang, Stephen Maybank.IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
• D2C: Deep Cumulatively and Comparatively Learning for Human Age EstimationKai Li, Junliang Xing, Weiming Hu, Stephen J Maybank.Pattern Recognition, 2017.
• Diagnosing Deep Learning Models for High Accuracy Age Estimation from a Single ImageJunliang Xing, Kai Li, Weiming Hu, Chunfeng Yuan, Haibin Ling.Pattern Recognition, 2017.
• Predicting Image Memorability by Multi-View Adaptive RegressionHouwen Peng, Kai Li, Bing Li, Haibin Ling, Weihua Xiong, Weiming Hu.ACM International Conference on Multimedia (ACM Multimedia), 2015.

获奖情况 :

• 人工智能顶级会议AAAI 2022卓越论文奖
• 2021全国空中博弈大赛二等奖
• 2020 CCF-腾讯犀牛鸟基金优秀奖
• 2020腾讯王者荣耀开悟邀请赛季军