Technical University of Munich

@inproceedings{buck_learning_2000,
 author = {S Buck and M Riedmiller},
 title = {Learning Situation Dependent Success Rates Of Actions In A {RoboCup}
	Scenario},
 booktitle = {Pacific Rim International Conference on Artificial Intelligence},
 year = {2000},
 pages = {809},
 abstract = {A quickly changing, not predictable environment complicates autonomous
	decision making in a system of mobile robots. To simplify action
	selection we suggest a suitable reduction of decision space by restricting
	the number of executable actions the agent can choose from. We use
	supervised neural learning to automaticly learn success rates of
	actions to facilitate decision making. To determine probabilities
	of success each agent relies on its sensory data. We show that using
	our approach it is possible to compute probabilities of success close
	to the real success rates of actions and further we give a few results
	of games of a {RoboCup} simulation team based on this approach.},
}