• Resumo

    O impacto da duração do episódio de avaliação na robótica evolutiva: uma análise qualitativa

    Data de publicação: 03/05/2023


    In evolutionary robotics, the combination of genetic
    algorithms, neural networks, and mobile robots is widespread.
    Recent studies revealed that the length of the agent evaluation
    episode and the manner in which it is varied during evolution
    directly affects the evolution performance and the behavior
    generated. The length of the episode influences both the accuracy
    of the fitness function as well as the quality of the generated
    behaviors. However, more investigation is still needed to better
    comprehend the full impact of different episode duration. The
    goal of this paper is to analyze and characterize the behaviors
    generated in robots evolved under varying the length of the
    evaluation episode. The agents all aim at solving locomotion
    problems. Results showed that reducing the episode length helps
    to develop initial behaviors that may perform fairly well, but are
    generally not fully developed. Longer evolutionary episodes tend
    to produce more static stay-at-a-fixed-position robotic behaviors,
    preventing further development. On the other hand, experiments
    that gradually increase episode duration over the course of
    evolution avoid the optimal static standing location, allowing
    more efficient behaviors to develop at the end of evolution.
    Key Words—Artificial intelligence, Adaptive robotics, Evolutionary

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