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