Machine Learning
Inductive Verification and Validation of the KULRoT RoboCup Team
RoboCup-98: Robot Soccer World Cup II
Learning Programs in the Event Calculus
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning Action Models for Reactive Autonomous Agents
Learning Action Models for Reactive Autonomous Agents
Adapting behavior by inductive prediction in soccer agents
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Skill acquisition via transfer learning and advice taking
ECML'06 Proceedings of the 17th European conference on Machine Learning
An overview on opponent modeling in RoboCup soccer simulation 2D
Robot Soccer World Cup XV
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The Robocup 2D simulation competition [13] proposes a dynamic environment where two opponent teams are confronted in a simplified soccer game. All major teams use a fixed algorithm to control its players. An unexpected opponent strategy, not previously considered by the developers, might result in winning all matches. To improve this we use ILP to learn action descriptions of opponent players; for learning on dynamic domains, we have to deal with the frame problem. The induced descriptions can be used to plan for desired field states. To show this we start with a simplified scenario where we learn the behaviour of a goalkeeper based on the actions of a shooter player. This description is used to plan for states where a goal can be scored. This result can directly be extended to a multiplayer environment.