Distributed dynamical systems such as Cellular Automata and Random Boolean Networks (and everything in between), have long been used as models to understand computation and self-replication in biology, morphogenesis, gene regulation, life-as-it-could-be, and the universe.
Such complex systems models have been extensively studied mathematically and experimentally in all their different variations, such as synchronous and asynchronous updates, dynamic automata networks that can grow and change their structure including components and interconnection topology, as well as their robustness.
In [1], A. Wuensche investigates the basins of attraction of cellular automata (CA) and random Boolean networks (RBN), and even suggests that they are The Ghost in the Machine.
Recent advances of such models, including continuous CA such as Lenia and neural-based CA, have been proposed as substrates to study the emergence of a more general intelligence [2, 3], thanks to their propensity to support properties such as self-organization, emergence, and open-endedness.
- What can we learn from Cellular Automata and Distributed Dynamical System models about intelligence?
- How can Cellular Automata and Distributed Dynamical System models be used to study the emergence of intelligence?
This workshop aims at bridging the gap between the ALife community working with CA and distributed dynamical systems, and the broader AI community interested in exploring concepts from complex systems/self-organization/artificial life for AI research and machine learning, including modular robotics such as voxel-based robots.
[1] Wuensche, A. (1994). The Ghost in the Machine: Basins of Attraction of Random Boolean Networks. Artificial Life III: SFI Studies in the Sciences of Complexity, vol. VII. Addison-Wesley.
[2] Hamon, G., Etcheverry, M., Chan, B. W. C., Moulin-Frier, C., & Oudeyer, P. Y. (2022). Learning sensorimotor agency in cellular automata.
[3] Gregor, K., & Besse, F. (2021). Self-organizing intelligent matter: A blueprint for an AI generating algorithm. arXiv preprint arXiv:2101.07627.