Computing Perceptual and Sensory-Motor Equivalence Classes for Map Learning
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چکیده
We discuss the problem of how to build up an internal representation for the uninterpreted data acquired by an agent dynamically coupled with its environment. The agent is endowed with a sensory-motor apparatus. Techniques based on Vector Quantization and Clustering Analysis are used to investigate both sensor and robot sensorymotor model spaces. The concepts of Perceptual and Sensory-Motor Equivalence Classes are introduced to partition the environment. The internal symbols obtained by such a procedure can be assumed to be grounded, since they have been directly derived from analyzing the interaction of the robot with the physical world.
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تاریخ انتشار 1996