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石兵

发布时间:2015-05-27     字体:[增加 减小]

 

    名:石兵

    别:男

出生年月:19829

    位:博士

    称:教授

E-mailbingshi@whut.edu.cn

个人简介:

石兵,博士,教授,博士生导师,湖北省楚天学子,计算机与人工智能学院副院长。本科及硕士毕业于南京大学计算机科学与技术系,博士毕业于英国南安普顿大学电子与计算机学院,并在南安普顿大学从事博士后研究工作。石兵博士是电子和电气工程师学会(IEEE)、国际计算机学会(ACM)和中国计算机学会(CCF)会员,主要从事人工智能、服务计算相关研究工作,在中国计算机学会(CCF)推荐会议和期刊发表论文30余篇,是多智能体系统权威会议AAMAS2017-2022程序委员会委员,并同时承担多个期刊的审稿工作。

主要研究方向:人工智能,服务计算等

石兵博士招收计算机、数学等相关学科博士、硕士研究生,有兴趣的同学可通过邮件联系。

近年主持的主要科研项目:

1.    GF预研基金项目:基于深度强化学习的XX建模方法研究

2.    深圳市科技创新委员会基础研究面上项目:面向随机需求的垄断云平台和竞争云平台定价机制研究

3.    教育部人文社会科学研究一般项目青年基金项目:面向随机需求的复杂环境下云资源定价机制研究

4.        教育部哲学社会科学研究后期资助项目:基于博弈论的双边拍卖市场交易策略研究

5.        国家自然科学基金青年基金:多双边拍卖下交易策略和竞争市场机制的研究

6.        教育部博士点基金新教师项目:复杂市场环境下智能Agent交易算法的理论研究和实现。

7.        教育部留学回国人员启动基金:基于博弈论的Web服务软件交互策略研究。

8.        软件新技术国家重点实验室开放基金:基于博弈论的网构软件交互策略研究。

另外还主持、参与其他纵向、横向科研项目10余项,包括国家科技部支撑计划等项目。

近几年发表的主要论文(2016-2022):

1.     Xu Z., Shi B., Joint Optimization of Trajectory and Frequency in Energy Constrained Multi-UAV Assisted MEC System, 20th International Conference on Service Oriented Computing, 2022. (CCF B)

2.     Shi B., Huang L., Shi R., A Deep Reinforcement Learning-based Approach for Pricing in the Competing Auction-based Cloud Market, Service Oriented Computing and Applications, 2022. (CCF C期刊)

3.     Shi B., Cao Z. Luo Y., A Deep Reinforcement Learning Based Dynamic Pricing Algorithm in Ride-hailing, 27th International Conference on Database Systems for Advanced Applications, 2022. (CCF B)

4.     Shi B., Song Z., Huang X., Xu J., User Incentive Based Bike-sharing Dispatching Strategy, 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2022. (Core A)

5.     Shi B., Deng Y., Yuan H., An Auction Based Task Dispatching and Pricing Mechanism in Bike-sharing. Knowledge-Based Systems, 235, 2022. (SCI, JCR Q1)

6.     Shi B., Chen F., Deep Reinforcement Learning based Task Offloading Strategy under Dynamic Pricing in Edge Computing, 19th International Conference on Service Oriented Computing, pp. 578-594, 2021. (CCF B)

7.     Shi B., Shi R., Li B., Multi-Agent Deep Reinforcement Learning Based Pricing Strategy for Competing Cloud Platforms in the Evolutionary Market, IEEE International Conference on Web Services, 2020. (CCF B)

8.     Shi B., Luo Y., Zhu L., Tang X., Liu B., Auction-Based Order-Matching Mechanisms to Maximize Social Welfare in Real-time Ride-sharing, 25th International Conference on Database Systems for Advanced Applications, 2020. (CCF B)

9.     Shi B., Huang L., Shi R., Pricing in the Competing Auction-based Cloud Market: A Multi-Agent Deep Deterministic Policy Gradient Approach, 18th International Conference on Service Oriented Computing, pp. 175-186,2020. (CCF B)

10.   Shi B., Zhu H., Yuan H., Shi R., Wang J., Pricing Cloud Resource based on Reinforcement Learning in the Competing Environment, 2018 International Conference on Cloud Computing, pp.158-171, 2018.

11.   Shi B., Wang J., and Wang Z., Huang Y., Trading Web Services in A Double Auction-Based Cloud Platform: A Game Theoretic Analysis, The 14th IEEE International Conference on Services Computing, pp.76-83, 2017.

12.   Shi B., Huang Y., Xiong S. and Gerding E., Setting An Effective Pricing Policy for Double Auction Marketplaces, The 14th Pacific Rim International Conference on Artificial Intelligence, pp.457-471, 2016. (最佳论文提名奖)

13.   Shi, B., Gerding, E. H. and Jennings, N. R., An Equilibrium Analysis of Trading Across Multiple Double Auction Marketplaces using Fictitious Play, Electronic Commerce Research and Applications, 17, pp134-149, 2016. (SCI, JCR Q1)