Even with improvements in machine learning enabling robots to
quickly optimize and perfect their skills, developing a seed skill from
which to begin an optimization remains a necessary challenge for large
action spaces. This thesis proposes a method for creating and using
such a seed by i) observing the effects of the actions of another robot,
ii) further optimizing the skill starting from this seed, and iii) em-
bedding the optimized skill in a full behavior. Called KSOBI, this
method is fully implemented and tested in the complex RoboCup 3D
simulation domain. The main result is a kick that, to the best of
our knowledge, kicks the ball farther in this simulator than has been
previously documented.Computer Science