Automated synthesis of analog electrical circuits by means ofgenetic programming
IEEE Transactions on Evolutionary Computation
Genetic Programming for Robot Soccer
RoboCup 2001: Robot Soccer World Cup V
Keepaway Soccer: A Machine Learning Testbed
RoboCup 2001: Robot Soccer World Cup V
Learning to Behave by Environment Reinforcement
RoboCup-99: Robot Soccer World Cup III
The RoboCup-98 Teamwork Evaluation Session: A Preliminary Report
RoboCup-99: Robot Soccer World Cup III
Genetic Programming of a Goal-Keeper Control Strategy for the RoboCup Middle Size Competition
Proceedings of the Second European Workshop on Genetic Programming
Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
RoboCup 2000: Robot Soccer World Cup IV
A Puzzle to Challenge Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Genetic team composition and level of selection in the evolution of cooperation
IEEE Transactions on Evolutionary Computation
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The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity and a lack of reliable strategic guidelines, the pervasive attitude has been that the problem can most successfully be attacked by human expertise, possibly assisted by some level of machine learning. This led, in RoboCup'97, to a field of simulator teams all of whose level and style of play were heavily influenced by the human designers of those teams. In contrast, our 1998 team was "designed" entirely by the process of genetic programming. Our evolved team placed in the middle of the pack at Robocup98, despite the fact that it was largely machine learned rather than hand coded. This paper presents our motivation, our approach, and the specific construction of our team that created itself from scratch.