Source:
Robocup-1998, Lecture Notes in Computer Science, Springer Verlag, Volume 1604, Paris, France, p.346--351 (1999)
ISBN:
3-540-66320-7
URL:
http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Teller_Astro.ps
Keywords:
genetic algorithms;
genetic programming
Abstract:
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. It is the thesis of our work that machine
learning, if given the opportunity to design (learn)
``everything'' about how the simulator team operates,
can develop a competitive simulator team that solves
the problem using highly successful, if largely non-
human, styles of play. To this end, Darwin United is a
team of eleven players that have been evolved as a team
of coordinated agents in the RoboCup simulator. Each
agent is given a subset of the lowest level perceptual
inputs and must learn to execute series of the most
basic actions (turn, kick, dash) in order to
participate as a member of the team. This paper
presents our motivation, our approach, and the specific
construction of our team that created itself from
scratch.