Autonomous Computation refers to a mode of computation in which behavior and processing emerge from within the system itself, rather than being centrally imposed from outside. The idea is associated with simple computational systems, cellular automata, mobile automata, and artificial life (ALife). A key example is the mobile automaton, where only one active cell is updated at a time, and this active point moves through space according to the rule set. This makes the computation strongly sequential and local, in contrast to cellular automata, where all cells are updated in parallel at each step.
The ALife context further emphasizes a bottom-up logic: instead of directly designing the global form, overall behavior arises from the accumulation of local rules and interactions. Even when rules are explicit, the outcome is not necessarily predictable. Different patterns and dynamics can appear from run to run, especially when probabilistic elements are introduced in the initial parameters. This is also linked to the Principle of Computational Equivalence, which suggests that complexity is not merely produced by advanced mechanisms such as parallel processing or large memory, but is inherent in computation itself. From this perspective, autonomous computation highlights how complex behavior can emerge from simple, powerful rules operating locally within a system.