Authors: Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang, Kenneth O. Stanley, Phillip Isola, David Ha
Abstract: With the recent Nobel Prize awarded for radical advances in protein
discovery, foundation models (FMs) for exploring large combinatorial spaces
promise to revolutionize many scientific fields. Artificial Life (ALife) has
not yet integrated FMs, thus presenting a major opportunity for the field to
alleviate the historical burden of relying chiefly on manual design and
trial-and-error to discover the configurations of lifelike simulations. This
paper presents, for the first time, a successful realization of this
opportunity using vision-language FMs. The proposed approach, called Automated
Search for Artificial Life (ASAL), (1) finds simulations that produce target
phenomena, (2) discovers simulations that generate temporally open-ended
novelty, and (3) illuminates an entire space of interestingly diverse
simulations. Because of the generality of FMs, ASAL works effectively across a
diverse range of ALife substrates including Boids, Particle Life, Game of Life,
Lenia, and Neural Cellular Automata. A major result highlighting the potential
of this technique is the discovery of previously unseen Lenia and Boids
lifeforms, as well as cellular automata that are open-ended like Conway’s Game
of Life. Additionally, the use of FMs allows for the quantification of
previously qualitative phenomena in a human-aligned way. This new paradigm
promises to accelerate ALife research beyond what is possible through human
ingenuity alone.
Source: http://arxiv.org/abs/2412.17799v1
