2,307 research outputs found
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Evolutionary Computation
This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field
Resilience and Controllability of Dynamic Collective Behaviors
The network paradigm is used to gain insight into the structural root causes
of the resilience of consensus in dynamic collective behaviors, and to analyze
the controllability of the swarm dynamics. Here we devise the dynamic signaling
network which is the information transfer channel underpinning the swarm
dynamics of the directed interagent connectivity based on a topological
neighborhood of interactions. The study of the connectedness of the swarm
signaling network reveals the profound relationship between group size and
number of interacting neighbors, which is found to be in good agreement with
field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl.
Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic
collective behaviors enabling us to uncover that the swarm signaling network is
a homogeneous clustered small-world network, thus facilitating emergent
outcomes if connectedness is maintained. Resilience of the emergent consensus
is tested by introducing exogenous environmental noise, which ultimately
stresses how deeply intertwined are the swarm dynamics in the physical and
network spaces. The availability of the signaling network allows us to
analytically establish for the first time the number of driver agents necessary
to fully control the swarm dynamics
Braitenberg Vehicles as Developmental Neurosimulation
The connection between brain and behavior is a longstanding issue in the
areas of behavioral science, artificial intelligence, and neurobiology.
Particularly in artificial intelligence research, behavior is generated by a
black box approximating the brain. As is standard among models of artificial
and biological neural networks, an analogue of the fully mature brain is
presented as a blank slate. This model generates outputs and behaviors from a
priori associations, yet this does not consider the realities of biological
development and developmental learning. Our purpose is to model the development
of an artificial organism that exhibits complex behaviors. We will introduce
our approach, which is to use Braitenberg Vehicles (BVs) to model the
development of an artificial nervous system. The resulting developmental BVs
will generate behaviors that range from stimulus responses to group behavior
that resembles collective motion. Next, we will situate this work in the domain
of artificial brain networks. Then we will focus on broader themes such as
embodied cognition, feedback, and emergence. Our perspective will then be
exemplified by three software instantiations that demonstrate how a BV-genetic
algorithm hybrid model, multisensory Hebbian learning model, and multi-agent
approaches can be used to approach BV development. We introduce use cases such
as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges
connecting behavioral and neural models (multisensory Hebbian learning model),
and cumulative classification (multi-agent approaches). In conclusion, we will
revisit concepts related to our approach and how they might guide future
development.Comment: 32 pages, 8 figures, 2 table
The Kinetic Basis of Morphogenesis
It has been shown recently (Shalygo, 2014) that stationary and dynamic
patterns can arise in the proposed one-component model of the analog
(continuous state) kinetic automaton, or kinon for short, defined as a
reflexive dynamical system with active transport. This paper presents
extensions of the model, which increase further its complexity and tunability,
and shows that the extended kinon model can produce spatio-temporal patterns
pertaining not only to pattern formation but also to morphogenesis in real
physical and biological systems. The possible applicability of the model to
morphogenetic engineering and swarm robotics is also discussed.Comment: 8 pages. Submitted to the 13th European Conference on Artificial Life
(ECAL-2015) on March 10, 2015. Accepted on April 28, 201
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