843 research outputs found
Self-repair ability of evolved self-assembling systems in cellular automata
Self-repairing systems are those that are able to reconfigure themselves following disruptions to bring them back into a defined normal state. In this paper we explore the self-repair ability of some cellular automata-like systems, which differ from classical cellular automata by the introduction of a local diffusion process inspired by chemical signalling processes in biological development. The update rules in these systems are evolved using genetic programming to self-assemble towards a target pattern. In particular, we demonstrate that once the update rules have been evolved for self-assembly, many of those update rules also provide a self-repair ability without any additional evolutionary process aimed specifically at self-repair
Robust Multi-Cellular Developmental Design
This paper introduces a continuous model for Multi-cellular Developmental
Design. The cells are fixed on a 2D grid and exchange "chemicals" with their
neighbors during the growth process. The quantity of chemicals that a cell
produces, as well as the differentiation value of the cell in the phenotype,
are controlled by a Neural Network (the genotype) that takes as inputs the
chemicals produced by the neighboring cells at the previous time step. In the
proposed model, the number of iterations of the growth process is not
pre-determined, but emerges during evolution: only organisms for which the
growth process stabilizes give a phenotype (the stable state), others are
declared nonviable. The optimization of the controller is done using the NEAT
algorithm, that optimizes both the topology and the weights of the Neural
Networks. Though each cell only receives local information from its neighbors,
the experimental results of the proposed approach on the 'flags' problems (the
phenotype must match a given 2D pattern) are almost as good as those of a
direct regression approach using the same model with global information.
Moreover, the resulting multi-cellular organisms exhibit almost perfect
self-healing characteristics
Artificial Gene Regulatory Network and Spatial Computation: A Case Study
International audienceThis paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular ArtiïŹcial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns
Artificial Gene Regulatory Networks and Spatial Computation: A Case Study
International audienceThis paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artificial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns
Interoceptive robustness through environment-mediated morphological development
Typically, AI researchers and roboticists try to realize intelligent behavior
in machines by tuning parameters of a predefined structure (body plan and/or
neural network architecture) using evolutionary or learning algorithms. Another
but not unrelated longstanding property of these systems is their brittleness
to slight aberrations, as highlighted by the growing deep learning literature
on adversarial examples. Here we show robustness can be achieved by evolving
the geometry of soft robots, their control systems, and how their material
properties develop in response to one particular interoceptive stimulus
(engineering stress) during their lifetimes. By doing so we realized robots
that were equally fit but more robust to extreme material defects (such as
might occur during fabrication or by damage thereafter) than robots that did
not develop during their lifetimes, or developed in response to a different
interoceptive stimulus (pressure). This suggests that the interplay between
changes in the containing systems of agents (body plan and/or neural
architecture) at different temporal scales (evolutionary and developmental)
along different modalities (geometry, material properties, synaptic weights)
and in response to different signals (interoceptive and external perception)
all dictate those agents' abilities to evolve or learn capable and robust
strategies
How to Color a French Flag--Biologically Inspired Algorithms for Scale-Invariant Patterning
In the French flag problem, initially uncolored cells on a grid must
differentiate to become blue, white or red. The goal is for the cells to color
the grid as a French flag, i.e., a three-colored triband, in a distributed
manner. To solve a generalized version of the problem in a distributed
computational setting, we consider two models: a biologically-inspired version
that relies on morphogens (diffusing proteins acting as chemical signals) and a
more abstract version based on reliable message passing between cellular
agents.
Much of developmental biology research has focused on concentration-based
approaches using morphogens, since morphogen gradients are thought to be an
underlying mechanism in tissue patterning. We show that both our model types
easily achieve a French ribbon - a French flag in the 1D case. However,
extending the ribbon to the 2D flag in the concentration model is somewhat
difficult unless each agent has additional positional information. Assuming
that cells are are identical, it is impossible to achieve a French flag or even
a close approximation. In contrast, using a message-based approach in the 2D
case only requires assuming that agents can be represented as constant size
state machines.
We hope that our insights may lay some groundwork for what kind of message
passing abstractions or guarantees, if any, may be useful in analogy to cells
communicating at long and short distances to solve patterning problems. In
addition, we hope that our models and findings may be of interest in the design
of nano-robots
Evolution and development of complex computational systems using the paradigm of metabolic computing in Epigenetic Tracking
Epigenetic Tracking (ET) is an Artificial Embryology system which allows for
the evolution and development of large complex structures built from artificial
cells. In terms of the number of cells, the complexity of the bodies generated
with ET is comparable with the complexity of biological organisms. We have
previously used ET to simulate the growth of multicellular bodies with
arbitrary 3-dimensional shapes which perform computation using the paradigm of
"metabolic computing". In this paper we investigate the memory capacity of such
computational structures and analyse the trade-off between shape and
computation. We now plan to build on these foundations to create a
biologically-inspired model in which the encoding of the phenotype is efficient
(in terms of the compactness of the genome) and evolvable in tasks involving
non-trivial computation, robust to damage and capable of self-maintenance and
self-repair.Comment: In Proceedings Wivace 2013, arXiv:1309.712
Artificial Gene Regulatory Networks and Spatial Computation: A Case Study
International audienceThis paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artificial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns
Unsupervised Learning of Echo State Networks: A case study in Artificial Embryogeny.
International audienceEcho State Networks (ESN) have demonstrated their efficiency in supervised learning of time series: a "reservoir" of neurons provide a set of dynamical systems that can be linearly combined to match the target dynamics, using a simple quadratic optimisation algorithm to tune the few free parameters. In an unsupervised learning context, however, another optimiser is needed. In this paper, an adaptive (1+1)-Evolution Strategy as well as the state-of-the-art CMA-ES are used to optimise an ESN to tackle the "ïŹag" problem, a classical benchmark from multi-cellular artificial embryogeny: the genotype is the cell controller of a Continuous Cellular Automata, and the phenotype, the image that corresponds to the ïŹxed point of the resulting dynamical system, must match a given 2D pattern. This approach is able to provide excellent results with few evaluations, and favourably compares to that using the NEAT algorithm (a state-of-the-art neuro-evolution method) to evolve the cell controllers. Some characteristics of the ïŹtness landscape of the ESN-based method are also investigated
Evolution of Self-Assembling Patterns in Cellular Automata using Development
This paper is concerned with the application of ideas inspired by developmental biology to the evolution of cellular automata rules using genetic programming. In particular, it is focused on so-called self-assembling patterns. The application of development in computing is reviewed, as is the evolutionary technique used in the paperâCartesian Genetic Programming. A novel developmental algorithm, termed the Developmental Cellular Model is introduced, and five sets of experiments on various self-assembly problems are detailed and the results examined
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