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@MISC{And_usingnon-determinism,
    author = {Thanut Sukkarnjanonth And and Prabhas Chongstitvatana},
    title = {Using Non-Determinism to Improve the Robustness of Robot Programs Generated by Genetic Programming},
    year = {}
}

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Abstract:

: Robustness is essential for programs generated by Genetic Programming (GP). This paper presents a method to improve the robustness. The method employs non-determinism in two ways: one is to evolve robot programs in noisy environments and another is to use probabilistic branch in the function set. The experiment is carried out on robot navigation problems. The result of the experiment shows that the robustness of robot programs has been improved. The analysis shows that the robustness is caused by the acquired "experience" and the amount of reuse of this experience while performing the task. Key words: Robustness, Robot programs, Non-determinism, Evolutionary Computation, Genetic Programming 1. Introduction Artificial Intelligence (AI) has been widely used to automatically generate programs. Genetic Programming (GP) [1] is one of methods that becomes popular in automatic generating programs. Our experiment use GP to generate programs for controlling a robot. GP is performed...

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