Meta-Diversity Search in Complex Systems, A Recipe for Artificial Open-Endedness ?

Mayalen Etcheverry (1) , Bert Wang-Chak Chan (2) , Clément Moulin-Frier (1) , Pierre-Yves Oudeyer (1)

1 Flowers - Flowing Epigenetic Robots and Systems
2 Chercheur indépendant

Can we build an artificial system that would be able to generate endless surprises if ran “forever” in Minecraft? While there is not a single path toward solving that grand challenge, this article presents what we believe to be some working ingredients for the endless generation of novel increasingly complex artifacts in Minecraft. Our framework for an open-ended system includes two components: a complex system used to recursively grow and complexify artifacts over time, and a discovery algorithm that leverages the concept of meta-diversity search. Since complex systems have shown to enable the emergence of considerable complexity from set of simple rules, we believe them to be great candidates to generate all sort of artifacts in Minecraft. Yet, the space of possible artifacts that can be generated by these systems is often unknown, challenging to characterize and explore. Therefore automating the long-term discovery of novel and increasingly complex artifacts in these systems is an exciting research field. To approach these challenges, we formulate the problem of meta-diversity search where an artificial “discovery assistant” incrementally learns a diverse set of representations to characterize behaviors and searches to discover diverse patterns within each of them. A successful discovery assistant should continuously seek for novel sources of diversities while being able to quickly specialize the search toward a new unknown type of diversity. To implement those ideas in the Minecraft environment, we simulate an artificial “chemistry” system based on Lenia continuous cellular automaton for generating artifacts, as well as an artificial “discovery assistant” (called Holmes) for the artifact-discovery process. 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.

Domaines: Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]; Automates cellulaires et gaz sur réseau [nlin.CG]

Mayalen Etcheverry, Bert Wang-Chak Chan, Clément Moulin-Frier, Pierre-Yves Oudeyer. Meta-Diversity Search in Complex Systems, A Recipe for Artificial Open-Endedness ?. 2021. ⟨hal-03354381v1⟩