Koopman-based Data-driven Soft Artificial Life: Obtaining Rulesets from Observed Data

Software-based artificial life methods use mathematical and computational models to mimic complexity in living systems. Although such methods seem promising pertaining to exploring emergent behaviour, obtaining the governing rulesets of such methods remains challenging. In this paper, we present a concept of combined use of methods targeting different levels/scales in an emergent behaviour to obtain software-based artificial life rulesets from observed data. Additionally, we investigate the consequences of using this combination of methods by proposing an instance of combining Cellular Automata (CA) and Agent-based modelling (ABM) with Koopman-based linearization. Our experiments on systems of Elementary Cellular Automaton (Rule 30), Game of Life (GOL), and Vicsek’s flocking show that the combined method can learn the overall non-linear and emergent behaviour, and the underlying governing rulesets. Our research also indicates that by identifying several emergent scales or levels in a system, the combined method has the potential to shed light on the learnt system dynamics.