56,396 research outputs found
The computer revolution in science: steps towards the realization of computer-supported discovery environments
The tools that scientists use in their search processes together form so-called discovery environments. The promise of artificial intelligence and other branches of computer science is to radically transform conventional discovery environments by equipping scientists with a range of powerful computer tools including large-scale, shared knowledge bases and discovery programs. We will describe the future computer-supported discovery environments that may result, and illustrate by means of a realistic scenario how scientists come to new discoveries in these environments. In order to make the step from the current generation of discovery tools to computer-supported discovery environments like the one presented in the scenario, developers should realize that such environments are large-scale sociotechnical systems. They should not just focus on isolated computer programs, but also pay attention to the question how these programs will be used and maintained by scientists in research practices. In order to help developers of discovery programs in achieving the integration of their tools in discovery environments, we will formulate a set of guidelines that developers could follow
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Evolving Gene Regulatory Networks with Mobile DNA Mechanisms
This paper uses a recently presented abstract, tuneable Boolean regulatory
network model extended to consider aspects of mobile DNA, such as transposons.
The significant role of mobile DNA in the evolution of natural systems is
becoming increasingly clear. This paper shows how dynamically controlling
network node connectivity and function via transposon-inspired mechanisms can
be selected for in computational intelligence tasks to give improved
performance. The designs of dynamical networks intended for implementation
within the slime mould Physarum polycephalum and for the distributed control of
a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1303.722
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
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