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yobo体育:石兵

来源:yobo体育 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

联系电话:13437138496

E-mail:bingshi@whut.edu.cn

 

个人简介:

石兵,博士,教授,湖北省楚天学子,计算机学院副院长。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,主要从事人工智能和多智能体系统的研究工作,在中国计算机学会(CCF)推荐会议和期刊发表论文20余篇,是多智能体系统权威会议AAMAS2017,AAMAS2018,AAMAS2019,AAMAS2020的程序委员会委员,并同时承担多个期刊的审稿工作。

主要研究方向:人工智能,多智能体系统,强化学习

 

石兵博士招收计算机、数学等相关学科硕士研究生,有兴趣的同学可通过邮件联系。

 

主持科研项目:

1.          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

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

3.          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

4.          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。

5.          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。

6.          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。

7.          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。

 

近几年发表论文(2016-2020):

1.      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)

2.      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) 

3.      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)

4.      Shi B., Li B., Maximizing Profits of Allocating Limited Resources under Stochastic User Demands, 25th IEEE International Conference on Parallel and Distributed Systems, pp.85-92, 2019.

5.      Shi B., Zhu L., Luo Y., Reverse Auction Based Incentive Order Matching Mechanism for Real-time Ride-sharing, The IEEE International Conference on Tools with Artificial Intelligence, 2019.

6.      Shi B., Yuan H., Shi R., Pricing Cloud Resource based on Multi-Agent Reinforcement Learning in the Competing Environment, 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 2018.

7.      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.

8.      Shi B., Zhu H., Wang J., and Sun B., Optimize Pricing Policy in Evolutionary Market with Multiple Proactive Competing Cloud Providers, The 29th International Conference on Tools with Artificial Intelligence, pp. 202-209, 2017.

9.      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.

10.      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)

11.  Shi B., Huang Y., Wang J. and Xiong S., A Game-Theoretic Analysis of Pricing Strategies for Competing Cloud Platforms, The 22nd IEEE International Conference on Parallel and Distributed Systems, pp.653-660, 2016.

12.  Shi B., Wang Z., Hu G., A Market-Based Analysis of Bidding Strategy Between Web Service Providers and Users, International Workshop on Management of Information, Processes and Cooperation, pp.75-86, 2016.

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)

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