I am a Ph.D. candidate in computer science studying multi-agent reinforcement learning (lying at the intersection of machine learning and game theory) under Dr. Isbell and Dr. Weiss. My main research agenda is concerned with how agents should act in the presence of other agents who are smart enough to be able to communicate but are not necessarily infinitely smart (rational). Thus I consider myself more part of the multi-agent learning community than the game theory or operation research communities, although the overlap is substantial. My work targets approximate optimality as opposed to regret or safety-value approaches.
My most recent published result (AAAI '11)
is an algorithm to find the complete set of correlated equilibria of a dynamic stochastic game (a.k.a. Markov game). The algorithm is akin to single-agent value-iteration.