余超

所属研究所、院系: 
先进网络与计算系统研究所
职称: 
副教授
E-mail: 
yuchao3@mail.sysu.edu.cn
办公地点: 
广州大学城中山大学南学院楼(管理学院楼)D504
教师简介: 

余超,中山大学“百人计划”引进副教授,国家“香江学者”。2007年本科毕业于华中科技大学电信系,获通信工程学士学位,2013年博士毕业于澳大利亚伍伦贡大学计算机系,获计算机博士学位。

主要从事强化学习、智能医疗、智能机器人、智能集群等方面的研究工作。先后在IEEE TNNLS, IEEE TCB,IEEE ITS, IEEE TVT,ACM T等国际期刊和IJCAI、AAMAS上发表学术论文60余篇,SCI检索20余篇。获最佳论文奖3次。主持科研项目10余项,经费共计300余万元。

担任SCI期刊IEICE Trans. Information and Systems和J. Systems Science and Engineering副主编;组织国际会议MATCSD2015,ACAN 2016,IEEE ICA2016,IEEE ICA2017,Special Track in PRICA20I8,DAI2019;担任国际会议AAAI2020, IJCAI2019,AAMAS2019,AAAI2019,AAMAS2018,PRICAI2018,AAMAS2017等国际会议PC。

强化学习是当前人工智能热潮的核心技术之一,在机器人与多机器人系统、健康医疗、广告推荐、对话系统、经济博弈等领域有着广泛的应用。目前,我们团队主要的科研项目有:(1)基于强化学习的机器人行为控制;(2)基于分布式强化学习的智能集群协同;(3)基于强化学习的智能医疗;(4)非完全信息博弈(德扑、战略游戏等);(5)大规模多智能体强化学习。欢迎有志于从事人工智能前沿研究的优秀本科生和研究生加入实验室

 

已毕业学生

研究生:王东旭(百度)、谭佳瑶(华为)、王鑫(中国农行、国家奖学金)、赵洪义(中国电子科技集团

本科生:李阳宁(爱丁堡大学)、杨天培(纽约大学)、吕柏杨(香港大学)、吕洪涛(上海交大直博、CCF优秀大学生奖)、冯湛搏(上海交大直博)、李豫晨(上海交大直博

研究领域: 

(1)多智能体系统理论:复杂网络、协同合作、博弈论、计算经济学、社会选择理论、概率图模型

(2)多智能体系统应用:自动驾驶、智能网联汽车、多车协同、电力市场、水利水电、类脑计算

(3)基于强化学习的机器人行为控制:深度强化学习、策略搜索、人机交互、迁移学习、虚实混合

(4)基于强化学习的智能集群技术:军事集群、有人无人协同、非完全信息博弈、涌现机制

(5)基于强化学习的智能医疗:慢性病治疗、重症室(脓毒症)决策、医疗影像、运动康复

海外经历: 
  • 2018.1-2019.6, 香港浸会大学/计算机系,研究员
  • 2010.9-2013.12,澳大利亚伍伦贡大学/计算机与软件工程系,博士
获奖及荣誉: 
  • 国家“香江学者”
  • 大连市高层次创新人才
  • 大连理工“星海学者”
  • 辽宁省自然科学学术成果奖(论文类)三等奖, 2018
  • 辽宁省自然科学学术成果奖(论文类)二等奖, 2016, 2017
  • 大连市自然科学优秀学术论文奖二等奖, 2017
  • 大连市自然科学优秀学术论文奖一等奖, 2016
  • 大连理工大学教学质量优良奖, 2016
  • 大连理工大学“优秀党员“, 2016
  • 大连理工大学“优秀工会工作积极分子”, 2016,2017
科研项目: 

主持项目:

“面向多智能体系统的博弈学习技术研究”,国家军委装备发展部“十三五”装备预研领域基金;

“基于强化学习的智能医疗”,国家“香江学者”项目 ;

“基于虚实混合的机器人强化学习关键技术研究”,大连市科技创新基金应用基础研究项目;

“机器人强化学习的关键技术研究”,大连市高层次人才创新支持计划项目;

“基于多智能体学习的社会规范涌现机制研究”,国家自然科学青年基金;

“基于复杂学习的一致性涌现机制研究”, 中国博士后基金特别资助;

“大规模分布式系统社会规范涌现机制研究”, 中国博士后面上基金项目;

“社会规范理论”, 中央高校基本科研业务经费;

“基于多智能体系统的社交物联网研究”,大连理工大学首届“星海骨干”人才培养项目;

核心参与项目:

”智能集群与有人无人分布式协同理论“,国家军委科技委前沿创新项目及应用;

“网联汽车群体智能决策理论与方法研究”,国家自然科学基金委员会-辽宁联合基金重点支持项目(U1808206);

“车联网关键技术”,国家863项目;

“非完全信息博弈理论”,某军委依托项目

主要学术兼职: 

Editor

       • Special Section on Frontiers in Agent-based Technology, IEICE Trans. Information and Systems.

       • Special Issue on Agent-Based Modelling for Complex Systems, J. Systems Science and Engineering.

Organisers

       • Workshop on Reinforcement Learning at DAI'19

      • Workshop on Methods and Applications of Reinforcement Learning @ PRICAI2018, Nanjing, China, August, 2018.

       • Special Track on Reinforcement Learning @ PRICAI2018, Nanjing, China, August, 2018.

      • IEEE ICA2017, 2nd IEEE International Conference on Agents (2017 IEEE ICA), Beijing, China, July 6-9, 2017.

      • IEEE ICA2016, IEEE International Conference on Agents (2016 IEEE ICA), Metsue, Japan, September 28-30, 2016.

      • ACAN 2016, The 8th International Workshop on Agent-based Complex Automated Negotiations (ACAN2016@AAMAS2016), Singapore, May 4, 2016.

      • MATCSD2015, Multi-Agent Technologies for Complex Systems Development: Challenges and Solutions, Dalian University of Technology, China, September 17-18, 2015.

PC

       IJCAI2019,AAMAS2019,AAAI2019,AAMAS2018,PRICAI2018,AAMAS2017

教授课程: 

1. 《强化学习》

2. 《多智能体系统》

3. 《推理与学习》

4. 《汇编语言》

代表性论著: 

期刊论文

  1. Chao Yu, JIming Liu and Shamim Nemati. Reinforcement Learning in Healthcare: A Survey arXiv preprint arXiv:1908.08796, 2019.
  2. Chao Yu, Xin Wang, Xin Xu, et al. Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs. IEEE Transactions Intelligent Transportation Systems, doi: 10.1109/TITS.2019.2893683, 2019. (IF:4.051)
  3.  Chao Yu, Jiming Liu and Hongyi Zhao. Inverse Reinforcement Learning for Intelligent Mechanical Ventilation and Sedative Dosing in Intensive Care Units. BMC Medical Informatics and Decision Making, 2019. (IF:2.134)
  4. Chao Yu, Yinzhao Dong and Jiming Liu, and Guoqi Ren. Incorporating Causal Factors into Reinforcement Learning for Dynamic Treatment Regimes in HIV. BMC Medical Informatics and Decision Making, 2019. (IF:2.134)
  5. Bingcai Chen, Chao Yu, Qishaui Diao, Rui Liu and Yuliang Wang. Social or Individual Learning? An Aggregated Solution for Coordination in Multiagent Systems. Journal of Systems Science and Systems Engineering, 27 (2), 180-200 (IF:0.766)
  6. Bingcai Chen, Xin Tao, Manrou Yang, Chao Yu, Weimin Pan, Victor C. M. Leung: A Saliency Map Fusion Method Based on Weighted DS Evidence Theory. IEEE Access 6: 27346-27355 (2018)
  7. Bingcai Chen, Zhenguo Gao, Manrou Yang, Qian Ning, Chao Yu, Weimin Pan, Mei Nian, Dongmei Xie: Packet Multicast in Cognitive Radio Ad Hoc Networks: A Method Based on Random Network Coding. IEEE Access 6: 8768-8781 (2018)
  8.  Fuxin Zhang, Guozhen Tan, Chao Yu. Fair Transmission Rate Adjustment in Cooperative Vehicle Safety Systems based on Multi-Agent Model Predictive Control. IEEE Transactions on Vehicular Technology. 66(7): 6115-6129, 2017. (IF:4.432)
  9. J Hao, J Sun, G Chen, Z Wang, Chao Yu, Z Ming, Efficient and Robust Emergence of Norms through Heuristic Collective Learning. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 12 (4), 23, 2017 (IF:1.216)
  10. Chao Yu, Guozhen Tan, Hongtao Lv, Zhen Wang, Jun Meng, Jianye Hao and Fenghui Ren. Modelling adaptive learning behaviours for consensus formation in human societies. Scientific Reports, 6, 2016. (IF:4.122)
  11. Chao Yu, Minjie Zhang, Fenghui Ren, and Guozhen Tan. Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas, IEEE Transactions on Neural Networks and Learning Systems. 26(12), 3083-3096, 2015. (IF:11.683)
  12. Chao Yu, Minjie Zhang, Fenghui Ren, and Guozhen Tan. Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems, IEEE Transactions on Cybernetics. 45(12), 2853-2867, 2015. (IF:10.387
  13. Chao Yu, Minjie Zhang and Fenghui Ren and Guozhen Tan. Emergence of Social Norms through Collective Learning in Networked Multiagent Systems, IEEE Transactions on Cybernetics, 44(12): 2342-2355, 2014. (IF:10.387)
  14. Chao Yu, Minjie Zhang and Fenghui Ren. Coordinated Learning by Exploiting Sparse Interaction in Multiagent Systems, Concurrency and Computation: Practice and Experience, 26(1): 51-70., 2014. (IF:1.114)
  15.  Zhen Wang, Chao Yu, Guanghai Cui, Yapeng Li, Mingchu Li, Evolution of Cooperation in Spatial Iterated Prisoner’s Dilemma Games under Localized Extremal Dynamics. Physica A: Statistical Mechanics and its Applications, 444:566-575, 2016. (IF:2.132)
  16. Jiankang Ren, Zichuan Xu, Chao Yu, Chi Lin, Guowei Wu, Guozhen Tan: Execution allowance based fixed priority scheduling for probabilistic real-time systems. Journal of Systems and Software 152: 120-133 (2019)
  17. Bingcai Chen, Zhongru Ren, Chao Yu, Iftikhar Hussain, Jintao Liu: Adversarial Examples for CNN-Based Malware Detectors. IEEE Access 7: 54360-54371 (2019)

会议论文

  1. Chao Yu, Guozhen Tan, The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control, IJCAI2019.
  2. Yaodong yang, Jianye Hao and Chao Yu,  Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Reinforcement Learning Framework. IJCAI2019
  3. Chao Yu, Xin Wang, Zhanbo Feng: Coordinated Multiagent Reinforcement Learning for Teams of Mobile Sensing Robots. AAMAS 2019: 2297-2299
  4.  Chao Yu, Guoqi Ren and Jiming Liu, Deep Inverse Reinforcement Learning for Sepsis Treatment, 2019 IEEE International Conference on Healthcare Informatics, 2019. (EI)
  5.  Chao Yu, Yinzhao Dong and Xin Wang, Multiagent Reinforcement Learning on Coordination Graphs, 4th International Workshop on Smart Simulation and Modelling for Complex Systems (SSMCS@IJCAI 2019). (Best Paper Award
  6.  Jiankang Ren, Xiaoyan Su, Guoqi Xie, Chao Yu, Guozhen Tan, Guowei Wu: Workload-Aware Harmonic Partitioned Scheduling of Periodic Real-Time Tasks with Constrained Deadlines. DAC 2019: 167:1-167:6
  7. Chao Yu, Dongxu Wang, Jiankang Ren, Hongwei Ge and Liang Sun. Decentralized Multiagent Reinforcement Learning for Efficient Robotic Control by Coordination Graphs. 15th Pacific Rim International Conference on Artificial Intelligence, pp. 191-203, 2018.
  8. Chao Yu, Dongxu Wang, Tianpei Yang, Wenxuan Zhu, Yuchen Li, Hongwei Ge and Jiankang Ren. Adaptively Shaping Reinforcement Learning Agents via Human Reward. 15th Pacific Rim International Conference on Artificial Intelligence, pp. 85-97, 2018. (Best Paper Nomination, 5 out of 441)
  9.  Chao Yu, Yatong Chen, Hongtao Lv, Jiankang Ren, Hongwei Ge and Liang Sun. Neural learning for the emergence of social norms in multiagent systems. 2017 IEEE International Conference on Agents (ICA), pp. 40-45, 2017.
  10.  Chao Yu, Hongtao Lv, Sandip Sen, Jianye hao, Fenghui Ren and Rui Liu. An Adaptive Learning Framework for Efficient Emergence of Social Norms. 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016), Singapore. pp. 1307-1308, 2016.
  11. Chao Yu, Hongtao Lv, Sandip Sen, Fenghui Ren and Guozhen Tan. Adaptive Learning for Efficient Emergence of Social Norms in Networked Multiagent Systems. In The Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016): Trends in Artificial Intelligence. LNAI 9810, pp. 805-818, 2016.
  12. Chao Yu, Rui Liu and Yuliang Wang. Social Learning in Networked Multiagent Systems. IEEE International Conference on Agents (2016 IEEE ICA), pp. 57-62, 2016.
  13. Chao Yu, Hongtao Lv, Honglin Bao, Jianye Hao and Zhen Wang. Hierarchical Learning for Emergence of Social Norms in Networked Multiagent Systems. In Proceedings of the 28th Australasian Joint Conference on Artificial Intelligence (AI2015).
  14.  Chao Yu, Hongtao Lv. Norm Emergence through Collective Learning and Information Diffusion in Complex Relationship Networks. In Proceedings of International Joint Agents Workshop and Symposium (IJAWS2015).
  15. Chao Yu, Minjie Zhang, Fenghui Ren and Jianye Hao. Individual and Social Learning for Norm Emergence in Networked Agent Societies. International Workshop on Multiagent Foundations of Social Computing co-located with the 12th International Conference on Autonomous Agents and Multiagent Systems (MFSC@AAMAS2014).
  16. Chao Yu, Minjie Zhang, Fenghui Ren and Xudong Luo. Emergence of Social Norms Through Collective Learning in Networked Agent Societies. The Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS2013) , pp.475-482, May 6-10, 2013, Saint Paul, USA.
  17. Chao Yu, Fenghui Ren and Minjie Zhang. An Adaptive Bilateral Negotiation Model Based on Bayesian Learning. The 4th AAMAS International Workshop on Agent-based Complex Automated Negotiations (ACAN@AAMAS2011), The Best Student Paper Award, Taipei, 2011