The University of Southampton
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fw6e11@ecs.soton.ac.uk

Dr Feng Wu

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Dr. Feng Wu is an Associate Researcher in the School of Computer Science and Technology at the University of Science and Technology of China (USTC). Before joining USTC in 2014, he was a Research Fellow at University of Southampton working with Prof. Nick Jennings on the ORCHID project. 

Research

Research interests

His research focuses on the foundations and applications of agent-based techniques, which allow complex systems to make decisions while coping with uncertainty, incomplete information, and limited computational resources. His research interests include planning under uncertaintymulti-agent systemsreinforcement learning, and robotics. He has published over 30 refereed papers on these topics in prestigious journals and conferences such as AIJJAAMASIJCAIAAAINIPSAAMASUAIICAPSECAI

Professional

Qualifications

He received a B.E. in Computer Science and Technology and a Ph.D. in Computer Application Technology (Co-advised by Prof. Xiaoping Chen and Prof. Shlomo Zilberstein) both from USTC in 2006 and 2011 respectively.

Professional activities

He served in the Program Committee of several AI conferences such as IJCAI (2011, 2015, 2016), AAAI (2011, 2013, 2015-2018), AAMAS (2013, 2014, 2016, 2017), and ICML (2016) and as Associate Chair of RoboCup 2015.

Publications

Wu, Feng, Jennings, Nicholas and Chen, Xiaoping (2012) Sample-based policy iteration for constrained DEC-POMDPs At ECAI 2012: 20th European Conference on Artificial Intelligence, France. 27 - 31 Aug 2012. , pp. 858-863.

Wu, Feng, Zilberstein, S and Jennings, N.R. (2013) Monte-Carlo expectation maximization for decentralized POMDPs At Proceedings of the 23rd International Joint Conference on AI (IJCAI), China. 03 - 09 Aug 2013. , pp. 397-403.

Bai, Aijun, Wu, Feng and Chen, Xiaoping (2013) Bayesian mixture modelling and inference based Thompson sampling in Monte-Carlo tree search At Advances in Neural Information Processing Systems (NIPS-13), United States. 05 - 10 Dec 2013.

Bai, Aijun, Wu, Feng, Zhang, Zongzhang and Chen, Xiaoping (2014) Thompson sampling based Monte-Carlo planning in POMDPs At Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS-14), United States. 21 - 26 Jun 2014.

Wu, Feng and Jennings, Nicholas R. (2014) Regret-based multi-agent coordination with uncertain task rewards At Proc. 28th Conf. on AI (AAAI). , pp. 1492-1499.

Jiang, W, Fischer, J, Greenhalgh, C, Ramchurn, Sarvapali, Wu, Feng, Jennings, Nicholas R. and Rodden, T (2014) Social implications of agent-based planning support for human teams At Int. Conf. on Collaboration Technologies and Systems. , pp. 310-317.

Ramchurn, Sarvapali, Wu, Feng, Fischer, Joel, Reece, Steven, Jiang, Wenchao, Roberts, Stephen J., Rodden, Tom and Jennings, Nicholas R. (2016) Human-agent collaboration for disaster response Autonomous Agents and Multi-Agent Systems, 30, (1), pp. 82-111.

Ramchurn, Sarvapali, Simpson, Edwin, Fischer, Joel, Huynh, Trung Dong, Ikuno, Yuki, Reece, Steven, Jiang, Wenchao, Wu, Feng, Flann, Jack, Roberts, S.J., Moreau, Luc, Rodden, T. and Jennings, N.R. (2015) HAC-ER: disaster response system based on human-agent collectives At 14th International Conference on Autonomous Agents and Multi-Agent Systems, Istanbul, Turkey. 04 - 08 May 2015. , pp. 533-541.

Ramchurn, Sarvapali, Fischer, Joel, Ikuno, Yuki, Wu, Feng, Flann, Jack and Waldock, Antony (2015) A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments At International Joint Conference on Artificial Intelligence.

Wu, Feng, Ramchurn, Sarvapali and Chen, Xiaoping (2016) Coordinating human-UAV teams in disaster response At International Joint Conference on Artificial Intelligence (IJCAI-16), United States. 09 - 15 Jul 2016. , pp. 1-7.

Ramchurn, Sarvapali D., Huynh, Trung Dong, Wu, Feng, Ikuno, Yuki, Flann, Jack, Moreau, Luc, Fischer, Joel, Jiang, Wenchao, Rodden, Tom, Simpson, Edwin, Reece, Steven, Roberts, Stephen and Jennings, Nicholas R. (2016) A disaster response system based on human-agent collectives Journal of Artificial Intelligence Research, 57, pp. 661-708. (doi:10.1613/jair.5098).

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