Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics
Thomas Michel, Marko Cvjetko, Gautier Hamon, Pierre-Yves Oudeyer, Cl\'ement Moulin-Frier

TL;DR
This paper introduces a curiosity-driven AI method to automatically discover and analyze diverse ecosystem dynamics in Flow-Lenia cellular automata, revealing complex self-organizing behaviors beyond previous approaches.
Contribution
The paper extends diversity search algorithms with curiosity-driven exploration in Flow-Lenia, enabling discovery of complex collective behaviors and ecosystem dynamics in large environments.
Findings
More diverse dynamics found compared to random search
Ecosystemic simulations reveal complex collective behaviors
Interactive tool supports human-AI collaborative exploration
Abstract
We present a method for the automated discovery of system-level dynamics in Flow-Lenia--a continuous cellular automaton (CA) with mass conservation and parameter localization-using a curiosity--driven AI scientist. This method aims to uncover processes leading to self-organization of evolutionary and ecosystemic dynamics in CAs. We build on previous work which uses diversity search algorithms in Lenia to find self-organized individual patterns, and extend it to large environments that support distinct interacting patterns. We adapt Intrinsically Motivated Goal Exploration Processes (IMGEPs) to drive exploration of diverse Flow-Lenia environments using simulation-wide metrics, such as evolutionary activity, compression-based complexity, and multi-scale entropy. We test our method in two experiments, showcasing its ability to illuminate significantly more diverse dynamics compared to…
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Taxonomy
TopicsScientific Computing and Data Management · Complex Systems and Time Series Analysis · Opinion Dynamics and Social Influence
