I will look at some of the often unstated principles common in multiagent learning research (and emergent communication work too), suggesting that they may be responsible for holding us back. In response, I will offer an alternative set of principles, which leads to the view of hindsight rationality, with connections to online learning and correlated equilibria. I will then describe some recent technical work understanding how we can build increasingly more powerful algorithms for hindsight rationality in sequential decision-making settings.

Speaker's Bio: Michael Bowling is a professor at the University of Alberta, a Fellow of the Alberta Machine Intelligence Institute, and a senior scientist in DeepMind. Michael led the Computer Poker Research Group, which built some of the best poker playing artificial intelligence programs in the world, including being the first to beat professional players at both limit and no-limit variants of the game. He also was behind the use of Atari 2600 games to evaluate the general competency of reinforcement learning algorithms and popularized research in Hanabi, a game that illustrates emergent communication and theory of mind.