Keyframe Sampling, Optimization, and Behavior Integration: A New Longest Kick in the RoboCup 3D Simulation League

dc.contributor.advisorStone, Peteren
dc.creatorDepinet, Mikeen
dc.date.accessioned2014-12-11T15:03:09Zen
dc.date.available2014-12-11T15:03:09Zen
dc.date.issued2014-05-02en
dc.description.abstractEven 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.en
dc.description.departmentComputer Sciencesen
dc.identifier.urihttp://hdl.handle.net/2152/27772en
dc.language.isoengen
dc.subjectroboticsen
dc.subjectRoboCup 3D simulationen
dc.subjectoptimizationen
dc.subjectseed skillen
dc.subjectkeyframe samplingen
dc.titleKeyframe Sampling, Optimization, and Behavior Integration: A New Longest Kick in the RoboCup 3D Simulation Leagueen
dc.typeThesisen

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