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Peter Gacs proposed a one-dimensional cellular automaton capable of a robust self-reproduction. Because the automaton is exceptionally large and complicated, very few people have ever succeeded in simulating it on a computer or analyzing its behavior. Here we demonstrate a partial simulation of Gacs' automaton (of Gray's version), discussing its robustness. We also discuss the potential applications of Gacs' framework., Comment: This paper was originally written in 2018 for submission to ALIFE 2018 and is published on arXiv without any revisions

A fundamental goal of astrobiology is to detect life outside of Earth. This proves to be an exceptional challenge outside of our solar system, where strong assumptions must be made about how life would manifest and interact with its planet. Such assumptions are required because of the lack of a consensus theory of living systems, or an understanding of the possible extent of planetary dynamics. Here we explore a model of life spreading between planetary systems via panspermia and terraformation. Our model shows that as life propagates across the galaxy, correlations emerge between planetary characteristics and location, and can function as a population-scale agnostic biosignature. This biosignature is agnostic because it is independent of strong assumptions about any particular instantiation of life or planetary characteristic--by focusing on a specific hypothesis of what life may do, rather than what life may be. By clustering planets based on their observed characteristics, and examining the spatial extent of these clusters, we demonstrate (and evaluate) a way to prioritize specific planets for further observation--based on their potential for containing life. We consider obstacles that must be overcome to practically implement our approach, including identifying specific ways in which better understanding astrophysical and planetary processes would improve our ability to detect life. Finally, we consider how this model leads us to think in novel ways about hierarchies of life and planetary scale replication., Comment: 16 pages, 20 figures

This paper reports on patterns exhibiting self-replication with spontaneous, inheritable mutations and exponential genetic drift in Neural Cellular Automata. Despite the models not being explicitly trained for mutation or inheritability, the descendant patterns exponentially drift away from ancestral patterns, even when the automaton is deterministic. While this is far from being the first instance of evolutionary dynamics in a cellular automaton, it is the first to do so by exploiting the power and convenience of Neural Cellular Automata, arguably increasing the space of variations and the opportunity for Open Ended Evolution.

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theoretical, synthetic and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas and contributions from a wide range of subjects. Hybrid Life is an attempt to bring attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. In particular, Hybrid Life focuses on three complementary themes: 1) theories of systems and agents, 2) hybrid augmentation, with augmented architectures combining living and artificial systems, and 3) hybrid interactions among artificial and biological systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022.

We present a new approach to exoplanet characterisation using techniques from complexity science, with potential applications to biosignature detection. This agnostic method makes use of the temporal variability of light reflected or emitted from a planet. We use a technique known as epsilon machine reconstruction to compute the statistical complexity, a measure of the minimal model size for time series data. We demonstrate that statistical complexity is an effective measure of the complexity of planetary features. Increasing levels of qualitative planetary complexity correlate with increases in statistical complexity and Shannon entropy, demonstrating that our approach can identify planets with the richest dynamics. We also compare Earth time series with Jupiter data, and find that for the three wavelengths considered, Earth's average complexity and entropy rate are approximately 50% and 43% higher than Jupiter's, respectively. The majority of schemes for the detection of extraterrestrial life rely upon biochemical signatures and planetary context. However, it is increasingly recognised that extraterrestrial life could be very different to life on Earth. Under the hypothesis that there is a correlation between the presence of a biosphere and observable planetary complexity, our technique offers an agnostic and quantitative method for the measurement thereof.