Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics
arXiv:2505.15998v4 Announce Type: replace Abstract: 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
The continuous evolution in AI research, particularly in autonomous discovery and complex system simulation, drives new advances in understanding self-organizing dynamics.
This research advances the capabilities of AI to autonomously explore and understand complex systems, which is crucial for developing more sophisticated AI agents and simulations.
AI systems can now independently discover complex emergent behaviors in continuous cellular automata, moving beyond pre-defined goals to driven curiosity in discovery.
- · AI research labs
- · Simulation & modeling platforms
- · Developers of AI agents
- · Traditional manual discovery methods
Improved understanding of emergent system dynamics through AI-driven exploration methods.
Development of more robust and adaptable AI agents capable of operating in highly dynamic and unpredictable environments.
Potential for AI-designed complex adaptive systems in various fields, from materials science to ecological modeling.
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Read at arXiv cs.AI