29,837 research outputs found
Energy and Complexity in Evolving Collective Robot Bodies and Brains
The impact of the environment on evolving increasingly complex morphologies (bodies) and controllers (brains) remains an open question in evolutionary biology and has important
implications for the evolutionary design of robots. This study uses
evolutionary robotics as an experimental platform to evaluate
relationships between environment complexity and evolving bodybrain complexity given energy costs on evolving complexity.
We evolve robot body-brain designs for increasingly complex
environments (difficult cooperative transport tasks) in a collective
robotic gathering simulation. The impact of complexity costs
on body-brain evolution is evaluated across such increasingly
complex environments. Results indicate that complexity costs
enable the evolution of simpler body-brain designs that are
effective in simple environments but yield negligible behavior
(task performance) differences in more complex environments
The evolutionary origins of hierarchy
Hierarchical organization -- the recursive composition of sub-modules -- is
ubiquitous in biological networks, including neural, metabolic, ecological, and
genetic regulatory networks, and in human-made systems, such as large
organizations and the Internet. To date, most research on hierarchy in networks
has been limited to quantifying this property. However, an open, important
question in evolutionary biology is why hierarchical organization evolves in
the first place. It has recently been shown that modularity evolves because of
the presence of a cost for network connections. Here we investigate whether
such connection costs also tend to cause a hierarchical organization of such
modules. In computational simulations, we find that networks without a
connection cost do not evolve to be hierarchical, even when the task has a
hierarchical structure. However, with a connection cost, networks evolve to be
both modular and hierarchical, and these networks exhibit higher overall
performance and evolvability (i.e. faster adaptation to new environments).
Additional analyses confirm that hierarchy independently improves adaptability
after controlling for modularity. Overall, our results suggest that the same
force--the cost of connections--promotes the evolution of both hierarchy and
modularity, and that these properties are important drivers of network
performance and adaptability. In addition to shedding light on the emergence of
hierarchy across the many domains in which it appears, these findings will also
accelerate future research into evolving more complex, intelligent
computational brains in the fields of artificial intelligence and robotics.Comment: 32 page
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Energy Flows in Low-Entropy Complex Systems
Nature's many complex systems--physical, biological, and cultural--are
islands of low-entropy order within increasingly disordered seas of
surrounding, high-entropy chaos. Energy is a principal facilitator of the
rising complexity of all such systems in the expanding Universe, including
galaxies, stars, planets, life, society, and machines. A large amount of
empirical evidence--relating neither entropy nor information, rather
energy--suggests that an underlying simplicity guides the emergence and growth
of complexity among many known, highly varied systems in the
14-billion-year-old Universe, from big bang to humankind. Energy flows are as
centrally important to life and society as they are to stars and galaxies. In
particular, the quantity energy rate density--the rate of energy flow per unit
mass--can be used to explicate in a consistent, uniform, and unifying way a
huge collection of diverse complex systems observed throughout Nature.
Operationally, those systems able to utilize optimal amounts of energy tend to
survive and those that cannot are non-randomly eliminated.Comment: 12 pages, 2 figures, review paper for special issue on Recent
Advances in Non-Equilibrium Statistical Mechanics and its Application. arXiv
admin note: text overlap with arXiv:1406.273
The use of information theory in evolutionary biology
Information is a key concept in evolutionary biology. Information is stored
in biological organism's genomes, and used to generate the organism as well as
to maintain and control it. Information is also "that which evolves". When a
population adapts to a local environment, information about this environment is
fixed in a representative genome. However, when an environment changes,
information can be lost. At the same time, information is processed by animal
brains to survive in complex environments, and the capacity for information
processing also evolves. Here I review applications of information theory to
the evolution of proteins as well as to the evolution of information processing
in simulated agents that adapt to perform a complex task.Comment: 25 pages, 7 figures. To appear in "The Year in Evolutionary Biology",
of the Annals of the NY Academy of Science
Evolutionary Robotics: a new scientific tool for studying cognition
We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment
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