2025/05/21 by Thomas Michel, Marko Cvjetko, Michel, Thomas +8 · 1 voice · 1 citation
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Opinion Dynamics and Social Influence #Scientific Computing and Data Management #cs.AI
paper · pdf · doi:10.48550/arxiv.2505.15998
openalex publication_date 2025/05/21 · arxiv published 2025/05/21 · openalex created_date 2025/10/19 · arxiv updated 2026/06/04 · openalex updated_date 2026/07/28
We present a curiosity-driven AI scientist method for discovering system-level dynamics in Flow-Lenia, a continuous cellular automaton (CA) with mass conservation and parameter localization. Building on prior work that uses diversity search in Lenia to find individual self-organized patterns, we adapt Intrinsically Motivated Goal Exploration Processes (IMGEPs) to large environments of interacting patterns, using simulation-wide metrics such as evolutionary activity, compression ratio, and multi-scale matter distribution. We apply IMGEP in two exploration experiments: one targeting ecosystem-level dynamics, the other matter movement through obstacle-laden environments. In both, IMGEP illuminates significantly more of the metric space than random search and reveals self-organized behaviors qualitatively resembling many biological phenomena. Leveraging the resulting archive, we then run a scaling study across six spatial scales and seven time horizons, uncovering macro-scale organization with no analogue at the base scale and characterizing how goal-space metrics behave at scale. This illustrates a strength of our approach: a relatively cheap large-scale diversity search can act as a principled scaffold for designing subsequent, more expensive experiments, enabling an iterative loop of experiment design, inspection, and redesign, supported by an interactive exploration tool that keeps scientists in the loop. Though demonstrated with Flow-Lenia, this approach potentially applies to other parameterizable complex systems where studying bottom-up collective behavior is of interest.