Analysis Of Multi-Robot Play Effectiveness And Of Distributed Incidental Play Recognition

Distributed play-based approaches have been proposed as an effective means of switching strategies during the course of timed, zero-sum games, such as robot soccer. However, play-based approaches have not yet been rigorously evaluated in a full robot soccer scenario. In this paper, we perform an extensive empirical analysis of play effectiveness with teams of robots. We show that different plays have a significant effect on opponent performance in real robot soccer games. In our analysis we further realized the problem of distributed play recognition: classifying the strategy being played by the opponent team. Play recognition in real robot soccer is a particularly challenging problem because our observations are only "incidental" - that is, the primary task of our team is to play soccer, not to explicitly observe members of the other team. Despite these challenges, we achieve high classification accuracy in the robot soccer domain. To achieve high accuracy, our team maintains a history of joint observations including team positions, opponent positions, and ball positions and utilizes bidden Markov models to recognize opponent plays.