1,279 research outputs found
Biomaker CA: a Biome Maker project using Cellular Automata
We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA).
In Biomaker CA, morphogenesis is a first class citizen and small seeds need to
grow into plant-like organisms to survive in a nutrient starved environment and
eventually reproduce with variation so that a biome survives for long
timelines. We simulate complex biomes by means of CA rules in 2D grids and
parallelize all of its computation on GPUs through the Python JAX framework. We
show how this project allows for several different kinds of environments and
laws of 'physics', alongside different model architectures and mutation
strategies. We further analyze some configurations to show how plant agents can
grow, survive, reproduce, and evolve, forming stable and unstable biomes. We
then demonstrate how one can meta-evolve models to survive in a harsh
environment either through end-to-end meta-evolution or by a more surgical and
efficient approach, called Petri dish meta-evolution. Finally, we show how to
perform interactive evolution, where the user decides how to evolve a plant
model interactively and then deploys it in a larger environment. We open source
Biomaker CA at: https://tinyurl.com/2x8yu34s .Comment: 20 pages, 23 figures. For code base, see https://tinyurl.com/2x8yu34
How Turing parasites expand the computational landscape of digital life
Why are living systems complex? Why does the biosphere contain living beings
with complexity features beyond those of the simplest replicators? What kind of
evolutionary pressures result in more complex life forms? These are key
questions that pervade the problem of how complexity arises in evolution. One
particular way of tackling this is grounded in an algorithmic description of
life: living organisms can be seen as systems that extract and process
information from their surroundings in order to reduce uncertainty. Here we
take this computational approach using a simple bit string model of coevolving
agents and their parasites. While agents try to predict their worlds, parasites
do the same with their hosts. The result of this process is that, in order to
escape their parasites, the host agents expand their computational complexity
despite the cost of maintaining it. This, in turn, is followed by increasingly
complex parasitic counterparts. Such arms races display several qualitative
phases, from monotonous to punctuated evolution or even ecological collapse.
Our minimal model illustrates the relevance of parasites in providing an active
mechanism for expanding living complexity beyond simple replicators, suggesting
that parasitic agents are likely to be a major evolutionary driver for
biological complexity.Comment: 13 pages, 8 main figures, 1 appendix with 5 extra figure
WLIMES, The Wandering LIMES: Towards a Theoretical Framework for Wandering Logic Intelligence Memory Evolutive Systems
This paper compares two complementary theories, Simeonov’s Wandering
Logic Intelligence and Ehresmann’s & Vanbremeersch’s Memory Evolutive
Systems, in view of developing a common framework for the study of multiscale
complex systems such as living systems. It begins by a brief summary
of WLI and MES, then analyzes their resemblances and differences. Finally,
the article provides an outlook for a future research
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
This article deals with the links between the enaction paradigm and
artificial intelligence. Enaction is considered a metaphor for artificial
intelligence, as a number of the notions which it deals with are deemed
incompatible with the phenomenal field of the virtual. After explaining this
stance, we shall review previous works regarding this issue in terms of
artifical life and robotics. We shall focus on the lack of recognition of
co-evolution at the heart of these approaches. We propose to explicitly
integrate the evolution of the environment into our approach in order to refine
the ontogenesis of the artificial system, and to compare it with the enaction
paradigm. The growing complexity of the ontogenetic mechanisms to be activated
can therefore be compensated by an interactive guidance system emanating from
the environment. This proposition does not however resolve that of the
relevance of the meaning created by the machine (sense-making). Such
reflections lead us to integrate human interaction into this environment in
order to construct relevant meaning in terms of participative artificial
intelligence. This raises a number of questions with regards to setting up an
enactive interaction. The article concludes by exploring a number of issues,
thereby enabling us to associate current approaches with the principles of
morphogenesis, guidance, the phenomenology of interactions and the use of
minimal enactive interfaces in setting up experiments which will deal with the
problem of artificial intelligence in a variety of enaction-based ways
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