Michael Bowling
Michael Bowling studies how computers learn to act in games and multi-agent environments. Bowling participated in and often led the CMUnited and CMDragons robot soccer teams from 1998 to 2003. The teams worked on motion, navigation, tracking, and team coordination. Bowling developed WoLF, or Win or Learn Fast, for reinforcement learning in multi-agent environments. His work includes a 2002 Artificial Intelligence journal publication and theoretical and practical follow-up work.
- Now at
- University of Alberta and Amii
- Works
- 296
- Citations
- 9,962
- h-index
- 48
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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1998–2003
Robot soccer teams
Bowling participated in and often led the CMUnited and CMDragons robot soccer teams from 1998 to 2003. The teams worked on motion, navigation, tracking, and team coordination.
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2002
Win or Learn Fast
Bowling developed WoLF, or Win or Learn Fast, for reinforcement learning in multi-agent environments. His work includes a 2002 Artificial Intelligence journal publication and theoretical and practical follow-up work.
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2008
Computer poker competition
Bowling led a team that defeated professional poker players in a man-versus-machine competition in 2008.
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2009
Monte Carlo regret methods
His Monte Carlo counterfactual regret work appeared at NeurIPS in 2009. It extended poker-specific methods into a family of solvers for zero-sum games with imperfect information.
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2021
Research recognition
AAAI elected Michael Bowling as a Fellow in 2021.
Sources and credits
Photo: www.amii.ca; creator not stated. License: Not stated by source. Source: amii.ca. Cropped to a square and resized. Ask us to remove or replace this photo.