Michael Bowling

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Michael Bowling [1] Michael H. Bowling,

an American computer scientist and full professor at the University of Alberta with research focusing on artificial intelligence, machine learning, games, and robotics. He received his Ph.D. on multiagent learning under advisor Manuela Veloso at Carnegie Mellon University, where he was involved in the RoboCup initiative. At UofA, he is leader of the Computer Poker Research Group [2], recently applying deep learning to the game of Poker with the AI agent DeepStack [3] [4] [5].

2000 …#

• Michael Bowling, Manuela M. Veloso (2001). Rational and Convergent Learning in Stochastic Games. IJCAI 2001
• Michael Bowling (2003). Multiagent Learning in the Presence of Agents with Limitations. Ph.D. thesis, Carnegie Mellon University, advisor Manuela M. Veloso
• Michael Bowling, Manuela M. Veloso (2003). Simultaneous Adversarial Multi-Robot Learning. IJCAI 2003
• Darse Billings, Aaron Davidson, Terence Schauenberg, Neil Burch, Michael Bowling, Robert Holte, Jonathan Schaeffer, Duane Szafron (2004). Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games. CG 2004
• Michael Bowling, Johannes Fürnkranz, Thore Graepel, Ron Musick (2006). Machine learning and Games. Machine Learning, Vol. 63, No. 3
• Umar Syed, Michael Bowling, Robert Schapire (2008). Apprenticeship learning using linear programming. ICML 2008, pdf
• Maria Cutumisu, Michael Bowling, Duane Szafron, Richard Sutton (2008). Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games. Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, pdf

2010 …#

• Joel Veness, Marc Lanctot, Michael Bowling (2011). Variance Reduction in Monte-Carlo Tree Search. NIPS, pdf
• Michael Bowling, Manuela M. Veloso (2011). Existence of Multiagent Equilibria with Limited Agents. CoRR, July 2011
• Michael Bowling, Neil Burch, Michael Johanson, Oskari Tammelin (2015). Heads-up limit hold’em poker is solved. Science, Vol. 347, No. 6218
• Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling (2017). DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science, Vol. 356, No. 6337

2020 …#

• Finbarr Timbers, Edward Lockhart, Martin Schmid, Marc Lanctot, Michael Bowling (2020). Approximate exploitability: Learning a best response in large games. arXiv:2004.09677
• Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D’Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot (2021). Solving Common-Payoff Games with Approximate Policy Iteration. arXiv:2101.04237

External Links#

• Michael Bowling’s Webpage

• Michael Bowling - Faculty of Science

• Michael Bowling - Department of Computing Science, University of Alberta - VideoLectures.NET

• Michael Bowling - The Mathematics Genealogy Project

• Michael Bowling - Google Scholar Citations

• Michael Bowling – Artificial Intelligence Goes All-In: Computers Playing Poker, March 17, 2017, YouTube Video

• DeepMind expands to Canada with new research office in Edmonton, Alberta by Demis Hassabis, DeepMind, July 5, 2017

References#

1. ↑ Michael Bowling - Faculty of Science
2. ↑ University of Alberta Computer Poker Research Group
3. ↑ Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling (2017). DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science, Vol. 356, No. 6337
4. ↑ DeepStack
5. ↑ GitHub - lifrordi/DeepStack-Leduc: Example implementation of the DeepStack algorithm for no-limit Leduc poker
6. ↑ dblp: Michael H. Bowling

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