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Learning Sensorimotor Capabilities in Cellular Automata

developmentalsystems.org · 14,967 words · saved by 1 readers

Novel classes of Cellular Automata (CA) have been recently introduced in the Artificial Life (ALife) community, able to generate a high diversity of complex self-organized patterns from local update rules. These patterns can display certain properties of biological systems such as a spatially localized organization, directional or rotational movements, etc. In fact, CA have a long relationship with biology and especially the origins of life/cognition as it is a self-organizing system that can serve as a computational testbed and toy model for such theories [1] but also as a source of inspiration on what are the basic building block of “life”. However, while the notions of embodiment within an environment, individuality1 and self-maintenance2 are central in theoretical biology and in particular in the definition of agency (e.g. Maturana & Varela [2] , Varela [3]), it remains unclear how such mechanisms and properties can emerge from a set of local update rules in a CA. In this blogpost,

Learning Sensorimotor Agency in Cellular Automata Finding robust self-organizing "agents" with gradient descent and curriculum learning: individuality, self-maintenance and sensori-motricity within a cellular automaton environment What you will find in this blog 📝 Abstract Novel classes of Cellular Automata (CA) have been recently introduced in the Artificial Life (ALife) community, able to generate a high diversity of complex self-organized patterns from local update rules. These patterns can display certain properties of biological systems such as a spatially localized organization,

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