Several anecdotal experimental observations suggest that physical constraints (e.g., in physics-based simulations of evolutionary robotics) can considerably increase the diversity of results obtained by evolutionary computation methods and can even yield surprises.

The metaphors that we choose to describe living and cognitive systems influence how we study them. Similarly, the behaviours that we choose as examples of intelligence or adaptivity influence the models we build and the ways we conceive of and study cognition. Over the course of history, humanity has embraced a variety of metaphors for minds and how they work. The dominant metaphor at the moment compares minds to computers. I would like to consider an alternative metaphor. Instead of thinking about brains as computers, and instead of thinking about cognition as problem-solving, what emerges when we compare mind (and life) to improvisational performance? This abstract uses improvisational drumming as a specific example to elaborate upon key aspects of enactivist and A-Life related perspectives of cognition. I suggest that (i) by recognising how improvised performances define their own norms as they develop, we can come to better understand how living and thinking systems might also define their own dynamic norms; (ii) by understanding what is lost when an acoustic drumset is replaced by one that is electronic, we can derive insight into what is missed when the body and the environment are left out of our descriptions of intelligent adaptive behaviour; and (iii) by recognising the diverse factors that determine the form of an improvised collaborative performance, we can better recognise the similarly diverse factors that sculpt the norms, behaviours and other features of an individual.

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. Le-nia, 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. A notebook with Flow Lenia implementation and demo are available at https://tinyurl.com/mr2ncy3h .