AGILO RoboCuppers 2002: Applying Cooperative Game State Estimation, Experience-based Learning, and Plan-based Control to Autonomous Robot Soccer

This paper describes the computational model underlying the AGILO autonomous robot soccer team and its implementation. The most salient aspects of the AGILO control software are that it includes (1) a cooperative probabilistic game state estimator working with a simple off-the-shelf camera system; (2) a situated action selection module that makes amble use of experience-based learning and produces coherent team behavior even if inter-robot communication is perturbed; and (3) a playbook executor that can perform preprogrammed complex soccer plays in appropriate situations by employing plan-based control techniques. The use of such sophisticated state estimation and control techniques characterizes the AGILO software. The paper discusses the computational techniques and necessary extensions based on experimental data from the 2001 robot soccer world championship.