Technical University of Munich

@article{beetz_agilo_2004,
 author = {M Beetz and T Schmitt and R Hanek and S Buck and F Stulp and D Schröter and B Radig},
 title = {The {AGILO} Robot Soccer Team – Experience-based Learning and Probabilistic
	Reasoning in Autonomous Robot Control},
 journal = {Autonomous Robots},
 year = {2004},
 volume = {17},
 pages = {55–77},
 number = {1},
 abstract = {This article describes the computational model underlying the {AGILO}
	autonomous robot soccer team, its implementation, and our experiences
	with it. According to our model the control system of an autonomous
	soccer robot consists of a probabilistic game state estimator and
	a situated action selection module. The game state estimator computes
	the robot's belief state with respect to the current game situation
	using a simple off-theshelf camera system. The estimated game state
	comprises the positions and dynamic states of the robot itself and
	its teammates as well as the positions of the ball and the opponent
	players. Employing sophisticated probabilistic reasoning techniques
	and exploiting the cooperation between team mates, the robot can
	estimate complex game states reliably and accurately despite incomplete
	and inaccurate state information. The action selection module selects
	actions according to specified selection criteria as well as learned
	experiences. Automatic learning techniques made it possible to develop
	fast and skillful routines for approaching the ball, assigning roles,
	and performing coordinated plays. The paper discusses the computational
	techniques based on experimental data from the 2001 robot soccer
	world championship.},
}