Michael Bowling

Algorithmic Game Theory & Opponent Modeling | Artificial Intelligence | Machine Learning | Reinforcement Learning | Robotics

Michael is fascinated by the problem of how computers can learn to play games through experience. He uses a variety of games, from poker to curling to Atari 2600, as a test-bed for new ideas and technologies, seeking to understand and predict how multiple actors learn and act in order to achieve their goals in an ambiguous or changing environment.

Michael's work can enhance understanding of competitive & cooperative environments in order to predict outcomes or optimize processes.