Information-theoretic approach to embodied category learning
We address the issue of how statistical and information-theoric measures can be employed to quantify the categorization process of a simulated robotic agent interacting with its local environment. We show how correlation, entropy, and mutual information can help identify distinct informational structure which can be used for object classiﬁcation. Further, by means of the isometric feature mapping algorithm, we analyze the weights of a neural network designed to ﬁnd clusters based on these distinct information theoretic characteristics of the object’s shape, size and color. We conclude that an understanding of the information-theoretic implications of categorization could help design robots with improved catego rization and better exploration strategies.
Keywords: Evolutionary Robotics
Record created on 2006-04-19, modified on 2016-08-08