Automating the Search for Artificial Life With Foundation Models.

Area of Science:

• Artificial Life (ALife)
• Foundation Models (FMs)
• Computational Biology

Background:

• Artificial Life research traditionally relies on manual design and trial-and-error for discovering simulation configurations.
• Foundation models have revolutionized protein discovery and offer potential for exploring complex combinatorial spaces in science.

Purpose of the Study:

• To integrate foundation models into Artificial Life research for automated discovery of lifelike simulations.
• To develop and demonstrate an approach (ASAL) that alleviates the burden of manual design in ALife.

Main Methods:

• Utilized vision-language foundation models to create an automated search for Artificial Life (ASAL).
• Applied ASAL across diverse ALife substrates: Boids, Particle Life, Game of Life, Lenia, and neural cellular automata.
• ASAL was designed to find simulations producing target phenomena, generating novelty, and illuminating diverse simulation spaces.

Main Results:

• Discovered previously unseen life-forms in Lenia and Boids simulations.
• Identified cellular automata exhibiting open-ended novelty, comparable to Conway's Game of Life.
• Enabled quantification of qualitative phenomena in a human-aligned manner through FM capabilities.

Conclusions:

• Automated search for Artificial Life (ASAL) represents a paradigm shift, leveraging foundation models for accelerated ALife discovery.
• This approach significantly enhances the exploration of ALife simulation spaces beyond human ingenuity alone.
• ASAL demonstrates broad applicability and effectiveness across various ALife systems.