I wonder if such artificial life forms could be hooked up to a machine learning algorithm somehow, so that they learn from their experience in the environment. I imagine the organisms would need some kind of "senses" like touch and vision; maybe a concept of food, light, enemy/friend..
Oh, there's a link at the bottom of the paper to a project called Sensorimotor Lenia, which goes in that direction.
> These patterns can display certain properties of biological systems such as a spatially localized organization, directional or rotational movements, etc. In fact, CA [cellular automata] 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 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, individuality and self-maintenance are central in theoretical biology and in particular in the definition of agency, it remains unclear how such mechanisms and properties can emerge from a set of local update rules in a CA.
> In this blogpost, we propose an approach enabling to learn self-organizing agents capable of reacting to the perturbations induced by the environment, i.e. robust agents with sensorimotor capabilities.
> ..Searching for rules at the cell-level in order to give rise to higher-level cognitive processes at the level of the organism and at the level of the group of organisms opens many exciting opportunities to the development of embodied approaches in AI in general.
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
Coupling artificial life with machine learning seems to produce mindblowing results. I encourage interested viewers to watch the Youtube video at the top of that page, it's really great.
Oh, there's a link at the bottom of the paper to a project called Sensorimotor Lenia, which goes in that direction.
> These patterns can display certain properties of biological systems such as a spatially localized organization, directional or rotational movements, etc. In fact, CA [cellular automata] 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 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, individuality and self-maintenance are central in theoretical biology and in particular in the definition of agency, it remains unclear how such mechanisms and properties can emerge from a set of local update rules in a CA.
> In this blogpost, we propose an approach enabling to learn self-organizing agents capable of reacting to the perturbations induced by the environment, i.e. robust agents with sensorimotor capabilities.
> ..Searching for rules at the cell-level in order to give rise to higher-level cognitive processes at the level of the organism and at the level of the group of organisms opens many exciting opportunities to the development of embodied approaches in AI in general.
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
https://developmentalsystems.org/sensorimotor-lenia/