Self-Replicating Machines in Continuous Space with Virtual Physics
Arnold Smith, Peter Turney, Robert Ewaschuk
Abstract
JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their external relationships are governed by a simulated physics that includes Brownian motion, viscosity, and spring-like attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary "seed" pattern is put in a "soup" of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life.
Create a lesson
Related papers
Position Paper: Neurotransmitters as a Missing Dimension in Artificial Neural Networks
Yupei Li, Manuel Milling, Berrak Sisman et al.
A Metaheuristic Optimization Framework for Discrete Optimization under Strict Time Limits
Umut Çalıkyılmaz, Nitin Nayak, Sven Groppe
Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization
Lu Han, Jin Wang, Yuchen Li et al.
A Spatiotemporal Extension of the Neuromorphic DBSCAN Implementation
Charles P. Rizzo, James S. Plank
Machine Zygote: Causal Biparental Heredity Before Learning in a Germline--Soma Artificial Agent
Lyes Saad Saoud
Bio-Inspired Palette Evolution in Indirectly Encoded Substrates: Timescale Compatibility Shapes Activation Function Discovery
Romain Claret, Michael O'Neill, Paul Cotofrei et al.