3 research outputs found
Vers l'adaptation de comportements par le biais de l'évolution culturelle pour des essaims de robots autonomes
Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization
The design of complex self-organising systems producing life-like phenomena,
such as the open-ended evolution of virtual creatures, is one of the main goals
of artificial life. Lenia, a family of cellular automata (CA) generalizing
Conway's Game of Life to continuous space, time and states, has attracted a lot
of attention because of the wide diversity of self-organizing patterns it can
generate. Among those, some spatially localized patterns (SLPs) resemble
life-like artificial creatures and display complex behaviors. However, those
creatures are found in only a small subspace of the Lenia parameter space and
are not trivial to discover, necessitating advanced search algorithms.
Furthermore, each of these creatures exist only in worlds governed by specific
update rules and thus cannot interact in the same one. This paper proposes as
mass-conservative extension of Lenia, called Flow Lenia, that solve both of
these issues. We present experiments demonstrating its effectiveness in
generating SLPs with complex behaviors and show that the update rule parameters
can be optimized to generate SLPs showing behaviors of interest. 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. We argue that this paves the way for the intrinsic evolution of
self-organized artificial life forms within continuous CAs
Flow Lenia: Mass conservation for the study of virtual creatures in continuous cellular automata
Lenia is a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states. Lenia has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate. Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures. However, those creatures are found in only a small subspace of the Lenia parameter space and are not trivial to discover, necessitating advanced search algorithms. We hypothesize that adding a mass conservation constraint could facilitate the emergence of SLPs. We propose here an extension of the Lenia model, called Flow Lenia, which enables mass conservation. We show a few observations demonstrating its effectiveness in generating SLPs with complex behaviors. Furthermore, we show how Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized. This allows for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules. We argue that this paves the way for the intrinsic evolution of self-organized artificial life forms within continuous CAs