Abstract
Embodied AI is a new approach to the design of autonomous intelligent systems. This chapter is about a new principle for the design of such systems that is deeply rooted in the notion of embodiment. Embodied action has causal effects on the nature and statistics of sensory inputs, which can in turn drive neural and cognitive processes. The statistics of sensory inputs can be captured by using methods from information theory, specifically measures of entropy, mutual information and complexity, on sensory data streams. Several such methods are outlined and their application to embodied AI systems is discussed.
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Sporns, O., Pegors, T.K. (2004). Information-Theoretical Aspects of Embodied Artificial Intelligence. In: Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds) Embodied Artificial Intelligence. Lecture Notes in Computer Science(), vol 3139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27833-7_5
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DOI: https://doi.org/10.1007/978-3-540-27833-7_5
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