Material and algorithmic constraints on the self-replication, growth, and evolution of feral machines

Future autonomous artificial and hybrid life forms (i.e., feral machines) will likely depend on self-organized automation for the design and manufacture of new individuals. This project aims to provide a modernized account of John von Neumann’s theory of self-replicating automata, incorporating recent advances in kinematic self-replication, nanoscale molecular machines, and engineered living systems such as Xenobots. The project seeks to identify the minimal material, informational, and algorithmic conditions necessary and sufficient for the self-replication and reproduction of cognitive and functional entities (i.e., robots) across multiple spatial and temporal scales. It will also investigate the algorithms capable of discovering such specifications for particular ecological or functional niches, while accounting for allometric scaling laws and thermodynamic costs of computation. As an initial proof of concept, a toy model of computational origins of life and self-replication has been developed using an algorithmic primordial soup implemented in the programming language Brainfuck (BF). In this system, self-replication emerges and remains stable across increasing program scales through hierarchically nested self-replicating subprograms. However, it remains unclear how such principles can be translated into scalable processes capable of enabling the self-reproduction, persistence, and open-ended evolution of machines at spatiotemporal scales comparable to those of human civilization. Central questions include whether complex self-replicating robots must be “grown” through developmental processes analogous to biological morphogenesis, and, if so, which physical substrates and algorithmic frameworks are most promising for achieving this.