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24 October 2005 Intelligent robots that adapt, learn, and predict
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Abstract
The purpose of this paper is to describe the concept and architecture for an intelligent robot system that can adapt, learn and predict the future. This evolutionary approach to the design of intelligent robots is the result of several years of study on the design of intelligent machines that could adapt using computer vision or other sensory inputs, learn using artificial neural networks or genetic algorithms, exhibit semiotic closure with a creative controller and perceive present situations by interpretation of visual and voice commands. This information processing would then permit the robot to predict the future and plan its actions accordingly. In this paper we show that the capability to adapt, and learn naturally leads to the ability to predict the future state of the environment which is just another form of semiotic closure. That is, predicting a future state without knowledge of the future is similar to making a present action without knowledge of the present state. The theory will be illustrated by considering the situation of guiding a mobile robot through an unstructured environment for a rescue operation. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
E. L. Hall, X. Liao, M. Ghaffari, and S. M. Alhaj Ali "Intelligent robots that adapt, learn, and predict", Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 600602 (24 October 2005); https://doi.org/10.1117/12.630920
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