If you made any changes in Pure these will be visible here soon.

Personal profile

Academic Biography

Yingke Chen received his PhD degree in the Department of Computer Science, Aalborg University in 2013. His research interests include Artificial Intelligence (in particular, multiagent learning and its applications in computer games) and Formal Methods (in particular, machine learning-based model checking). Most of his work appears in well-known AI/ML journals and prestigious AI/FM conferences. He serves in program committee for top international conferences like IJCAI and AAMAS, and reviews articles for a wide range of journals including several IEEE Transactions.

Besides conducting fundamental research, he is also interested in addressing emerging business challenges with AI/ML techniques. So far, he has been involved in several Innovate UK funded Knowledge Transfer Partnerships (KTP): A Game-based E-learning Platform for Language Training, 2018-2020; Collaborative Planning for Hybrid AUV (Autonomous Underwater Vehicle) Systems, 2019-2021.

Fingerprint Dive into the research topics where Yingke Chen is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 1 Similar Profiles
Planning Engineering & Materials Science
Model checking Engineering & Materials Science
Decision making Engineering & Materials Science
Scalability Engineering & Materials Science
Learning algorithms Engineering & Materials Science
Temporal logic Engineering & Materials Science
Reinforcement learning Engineering & Materials Science
Traffic congestion Engineering & Materials Science

Research Output 2014 2019

  • 5 Paper
  • 4 Article
  • 3 Conference contribution

Self-Improving Generative Adversarial Reinforcement Learning

Liu, Y., Zeng, Y., Chen, Y. & Tang, J., 15 May 2019.

Research output: Contribution to conferencePaperResearchpeer-review

48 Downloads (Pure)

Decision-Theoretic Planning Under Anonymity in Agent Populations

Sonu, E., Chen, Y. & Doshi, P., 24 Aug 2017, In : Journal of Artificial Intelligence Research. 59, p. 725-770

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Planning
Traffic congestion
Scalability
Polynomials
Data storage equipment

On Markov Games Played by Bayesian and Boundedly-Rational Players

Chandrasekaran, M., Chen, Y. & Doshi, P., 13 Feb 2017, Proceedings of 31st AAAI Conference on Artificial Intelligence. AAAI, 7 p. ( Proceedings of the AAAI conference on artificial intelligence).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

Open Access
File
Incomplete information
Constraint satisfaction problem
Bounded rationality
Common priors
50 Downloads (Pure)

Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams

Chandrasekaran, M., Doshi, P., Zeng, Y. & Chen, Y., 23 Nov 2016, In : Autonomous Agents and Multi-Agent Systems. p. -

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Planning
Decision making
Reinforcement learning
Dynamic models
Communication

Interactive dynamic influence diagrams for relational agents

Pan, Y., Chen, Y., Tang, J. & Zeng, Y., 2 Feb 2016, Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015. Institute of Electrical and Electronics Engineers Inc., p. 233-234 2 p. 7397467

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

Decision making