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Found 10 papers, 9 papers with code
From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved.
Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs
Biological nervous systems are created in a fundamentally different way than current artificial neural networks.
MarioGPT: Open-Ended Text2Level Generation through Large Language Models
MarioGPT can not only generate diverse levels, but can be text-prompted for controllable level generation, addressing one of the key challenges of current PCG techniques.
Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems
Inspired by cellular growth and self-organization, Neural Cellular Automata (NCAs) have been capable of "growing" artificial cells into images, 3D structures, and even functional machines.
HyperNCA: Growing Developmental Networks with Neural Cellular Automata
In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process.
A Unified Substrate for Body-Brain Co-evolution
The genome of such a multicellular organism guides the development of its body from a single cell, including its control system.
Growing 3D Artefacts and Functional Machines with Neural Cellular Automata
Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells.
EvoCraft: A New Challenge for Open-Endedness
In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion.
Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning
We test this hypothesis by decoupling the number of Hebbian learning rules from the number of synapses and systematically varying the number of Hebbian learning rules.
Meta-Learning through Hebbian Plasticity in Random Networks
We find that starting from completely random weights, the discovered Hebbian rules enable an agent to navigate a dynamical 2D-pixel environment; likewise they allow a simulated 3D quadrupedal robot to learn how to walk while adapting to morphological damage not seen during training and in the absence of any explicit reward or error signal in less than 100 timesteps.
