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Neil Burch

Neil Burch

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

Google Scholar

About

Neil Burch works on decision making in imperfect information environments, where different agents know different things about the world. What are the best choices an agent can make in theory? What are practical algorithms for making decisions? How close are those things? He is particularly interested in applying search to these problems, using computation at decision time to make better decisions.

Awards

  • Development of depth-limited imperfect information search, no-limit agent beat professional poker players, 2016
  • Solved checkers, 2007

Relevant Publications

  • Bard, N., Foerster, J.N, Chandar, S., Burch, N., Lanctot, M., Song, H. F., Parisotto, E., Dumoulin, V., Moitra, S., Hughes, E., Dunning, I., Mourad, S., Larochelle, H., Bellemare, M.G., Bowling, M. (2019). The Hanabi challenge: A new frontier for AI research, 32.

  • Moravčík, M., Schmid, M., Burch, N., Lisý, V, Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., Bowling, M. (2017). DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker. 

  • Schmid, M., Burch, N., Lanctot, M., Moravcik, M., Kadlec, R., Bowling, M. (2018). Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines.

  • Bowling, M., Burch, N., Johanson, M., Tammelin, O. (2017). Heads-up Limit Hold’em Poker is Solved.

Institution

Amii

University of Alberta

Department

Computing Science

Education

  • PhD (Computing Science), University of Alberta
  • MSc (Computing Science), University of Alberta
  • BSc (computing Science), University of Alberta

Country

Canada

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