197,926 research outputs found
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
Digital ecosystems
We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which
are considered to be robust, self-organising and scalable architectures that can automatically
solve complex, dynamic problems. So, this work is concerned with the creation, investigation,
and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological
ecosystems. First, we created the Digital Ecosystem, a novel optimisation technique inspired
by biological ecosystems, where the optimisation works at two levels: a first optimisation,
migration of agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on evolutionary computing
that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant
constraints. We then investigated its self-organising aspects, starting with an extension
to the definition of Physical Complexity to include the evolving agent populations of our
Digital Ecosystem. Next, we established stability of evolving agent populations over time,
by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics.
Further, we evaluated the diversity of the software agents within evolving agent populations,
relative to the environment provided by the user base. To conclude, we considered alternative
augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating
effect of a clustering catalyst on the evolutionary dynamics of our Digital Ecosystem, through
the direct acceleration of the evolutionary processes. We also studied the optimising effect of
targeted migration on the ecological dynamics of our Digital Ecosystem, through the indirect
and emergent optimisation of the agent migration patterns. Overall, we have advanced the
understanding of creating Digital Ecosystems, the self-organisation that occurs within them,
and the optimisation of their Ecosystem-Oriented Architecture
Digital Ecosystems: Self-Organisation of Evolving Agent Populations
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. Self-organisation is perhaps one of the most
desirable features in the systems that we engineer, and it is important for us
to be able to measure self-organising behaviour. We investigate the
self-organising aspects of Digital Ecosystems, created through the application
of evolutionary computing to Multi-Agent Systems (MASs), aiming to determine a
macroscopic variable to characterise the self-organisation of the evolving
agent populations within. We study a measure for the self-organisation called
Physical Complexity; based on statistical physics, automata theory, and
information theory, providing a measure of information relative to the
randomness in an organism's genome, by calculating the entropy in a population.
We investigate an extension to include populations of variable length, and then
built upon this to construct an efficiency measure to investigate clustering
within evolving agent populations. Overall an insight has been achieved into
where and how self-organisation occurs in our Digital Ecosystem, and how it can
be quantified.Comment: 5 pages, 5 figures, ACM Management of Emergent Digital EcoSystems
(MEDES) 200
Mobile Computing in Digital Ecosystems: Design Issues and Challenges
In this paper we argue that the set of wireless, mobile devices (e.g.,
portable telephones, tablet PCs, GPS navigators, media players) commonly used
by human users enables the construction of what we term a digital ecosystem,
i.e., an ecosystem constructed out of so-called digital organisms (see below),
that can foster the development of novel distributed services. In this context,
a human user equipped with his/her own mobile devices, can be though of as a
digital organism (DO), a subsystem characterized by a set of peculiar features
and resources it can offer to the rest of the ecosystem for use from its peer
DOs. The internal organization of the DO must address issues of management of
its own resources, including power consumption. Inside the DO and among DOs,
peer-to-peer interaction mechanisms can be conveniently deployed to favor
resource sharing and data dissemination. Throughout this paper, we show that
most of the solutions and technologies needed to construct a digital ecosystem
are already available. What is still missing is a framework (i.e., mechanisms,
protocols, services) that can support effectively the integration and
cooperation of these technologies. In addition, in the following we show that
that framework can be implemented as a middleware subsystem that enables novel
and ubiquitous forms of computation and communication. Finally, in order to
illustrate the effectiveness of our approach, we introduce some experimental
results we have obtained from preliminary implementations of (parts of) that
subsystem.Comment: Proceedings of the 7th International wireless Communications and
Mobile Computing conference (IWCMC-2011), Emergency Management: Communication
and Computing Platforms Worksho
Ab Initio Modeling of Ecosystems with Artificial Life
Artificial Life provides the opportunity to study the emergence and evolution
of simple ecosystems in real time. We give an overview of the advantages and
limitations of such an approach, as well as its relation to individual-based
modeling techniques. The Digital Life system Avida is introduced and prospects
for experiments with ab initio evolution (evolution "from scratch"),
maintenance, as well as stability of ecosystems are discussed.Comment: 13 pages, 2 figure
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