Action selection via learning behavior patterns in multi-robot domains

Abstract

The RoboCup robot soccer Small Size League has been running since 1997 with many teams success-fully competing and very effectively playing the games. Teams of five robots, with a combined au-tonomous centralized perception and control, and distributed actuation, move at high speeds in the field space, actuating a golf ball by passing and shooting it to aim at scoring goals. Most teams run their own pre-defined team strategies, unknown to the other teams, with flexible game-state dependent assignment of robot roles and positioning. How-ever, in this fast-paced noisy real robot league, rec-ognizing the opponent team strategies and accord-ingly adapting one’s own play has proven to be a