Learning Game Strategies by Experimentation

Deliberative experimental learning is an approach for learning explicit game strategies in a small number of trials by posting and experimentally satisfying learning goals. Learning explicit strategies is important for producing knowledge that can easily be transferred via analogy to new games, as well as for rapid learning. In our approach, experiments, or plans for learning, serve to drive both exploration and credit assignment, by helping to explain the execution trace. We describe a system that learns strategic plans for a subset of games in the General Game Playing (GGP) framework and present experimental results showing that it learns to win most of these games in fewer than 10 trials.