Visualizing the Structure of Lenia Parameter Space
This work addresses fundamental open problems in the theoretical understanding of Lenia for researchers in cellular automata and complex systems, though it is incremental as it builds on existing methods for classification and visualization.
The authors tackled the challenge of understanding the behavior of Lenia, a continuous cellular automaton, by developing a method to automatically classify systems into four dynamical classes, which enabled detection of moving solitons and visualization of the parameter space structure, revealing new soliton families in unexpected parameter regions and universal phase space patterns across different kernels.
Continuous cellular automata are rocketing in popularity, yet developing a theoretical understanding of their behaviour remains a challenge. In the case of Lenia, a few fundamental open problems include determining what exactly constitutes a soliton, what is the overall structure of the parameter space, and where do the solitons occur in it. In this abstract, we present a new method to automatically classify Lenia systems into four qualitatively different dynamical classes. This allows us to detect moving solitons, and to provide an interactive visualization of Lenia's parameter space structure on our website https://lenia-explorer.vercel.app/. The results shed new light on the above-mentioned questions and lead to several observations: the existence of new soliton families for parameters where they were not believed to exist, or the universality of the phase space structure across various kernels.