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Automating the Search for Artificial Life With Foundation Models.

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Summary
This summary is machine-generated.

Foundation models (FMs) now accelerate Artificial Life (ALife) research by automating the discovery of novel simulations. This approach, ASAL, moves beyond manual trial-and-error, uncovering diverse and open-ended lifelike systems.

Keywords:
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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.