ArticleScience advances2025
Discovering sensorimotor agency in cellular automata using diversity search.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of artificial life studies how life-like phenomena such as agency and self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open question is whether it is possible to find environment rules that self-organize robust "individuals" from an initial state with no prior existence of things like "bodies," "brain," "perception," or "action." Here, we leverage recent advances in machine learning, combining algorithms for diversity search, curriculum learning, and gradient descent, to automate the search of such "individuals." We show that this approach enables us to systematically find environmental conditions in CA leading to self-organization of basic forms of agency, i.e., localized structures that move around and react in a coherent and highly robust manner to external obstacles, maintain their integrity, and have strong capabilities to generalize to new environments. We discuss how this approach opens new perspectives in artificial intelligence and synthetic bioengineering.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.