Michael Bowling, University of Alberta
Assistant Professor
Computing Science
Edmonton, Alberta
bowling@cs.ualberta.ca
Office:
(780) 492-1766
Bio/Research
I am interested in the general problem of goal-directed decision making among interacting intelligent agents.
I am particularly interested in learning and planning in ad-hoc multiagent systems, which are also colloquially known as "pickup games". Humans are adept at facing and adapting t...
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I am particularly interested in learning and planning in ad-hoc multiagent systems, which are also colloquially known as "pickup games". Humans are adept at facing and adapting t...
Click to Expand >>
Bio/Research
I am interested in the general problem of goal-directed decision making among interacting intelligent agents.
I am particularly interested in learning and planning in ad-hoc multiagent systems, which are also colloquially known as "pickup games". Humans are adept at facing and adapting to previously unseen situations. This characteristic is prominent in recreational pickup games, where often completely unknown players are divided into teams for competition. Human players quickly assess and adapt to their teammates' and opponents' capabilities and styles of play. I am interested in investigating machine learning and planning techniques, for both assessment and adaptation, that enable agents to handle the challenge of interacting with other previously unknown agents.
I am pursuing these issues in robot domains, in particlar robot soccer, as well as commercial computer games.
Click to Shrink <<
I am particularly interested in learning and planning in ad-hoc multiagent systems, which are also colloquially known as "pickup games". Humans are adept at facing and adapting to previously unseen situations. This characteristic is prominent in recreational pickup games, where often completely unknown players are divided into teams for competition. Human players quickly assess and adapt to their teammates' and opponents' capabilities and styles of play. I am interested in investigating machine learning and planning techniques, for both assessment and adaptation, that enable agents to handle the challenge of interacting with other previously unknown agents.
I am pursuing these issues in robot domains, in particlar robot soccer, as well as commercial computer games.
Click to Shrink <<