摘要 This work presents a novel learning method in the

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原文連結

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論文資訊

• 類型：已發表論文
• 日期：2010

摘要

This work presents a novel learning method in the context of embodied 人工智慧 and 自組織, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive 資訊 in the sensorimotor loop. It is evaluated on robot chains of varying length with individually controlled, noncommunicating segments. The comparison of the results shows that maximizing the predictive 資訊 per wheel leads to a higher coordinated behavior of the physically connected robots compared with a maximization per robot. Another focus of this article is the analysis of the effect of the robot chain length on the overall behavior of the robots. It will be shown that longer chains with less capable controllers outperform those o

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