1 code implementation • 14 Feb 2024 • Gautier Hamon, Mayalen Etcheverry, Bert Wang-Chak Chan, Clément Moulin-Frier, Pierre-Yves Oudeyer
The research field of Artificial Life studies how life-like phenomena such as autopoiesis, agency, or self-regulation can self-organize in computer simulations.
no code implementations • 1 Dec 2023 • Mayalen Etcheverry, Bert Wang-Chak Chan, Clément Moulin-Frier, Pierre-Yves Oudeyer
Holmes incrementally learns a hierarchy of modular representations to characterize divergent sources of diversity and uses a goal-based intrinsically-motivated exploration as the diversity search strategy.
1 code implementation • 17 Jul 2023 • Mayalen Etcheverry, Michael Levin, Clément Moulin-Frier, Pierre-Yves Oudeyer
Advances in bioengineering and biomedicine demand a deep understanding of the dynamic behavior of biological systems, ranging from protein pathways to complex cellular processes.
1 code implementation • 14 Dec 2022 • Erwan Plantec, Gautier Hamon, Mayalen Etcheverry, Pierre-Yves Oudeyer, Clément Moulin-Frier, Bert Wang-Chak Chan
Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules.
1 code implementation • NeurIPS 2020 • Mayalen Etcheverry, Clement Moulin-Frier, Pierre-Yves Oudeyer
Self-organization of complex morphological patterns from local interactions is a fascinating phenomenon in many natural and artificial systems.
no code implementations • 13 May 2020 • Mayalen Etcheverry, Pierre-Yves Oudeyer, Chris Reinke
A central challenge is how to learn incrementally representations in order to progressively build a map of the discovered structures and re-use it to further explore.
no code implementations • ICLR 2020 • Chris Reinke, Mayalen Etcheverry, Pierre-Yves Oudeyer
Using a continuous GOL as a testbed, we show that recent intrinsically-motivated machine learning algorithms (POP-IMGEPs), initially developed for learning of inverse models in robotics, can be transposed and used in this novel application area.