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تسجيل مستخدم جديدDigital Ecosystems: Optimisation by a Distributed Intelligence
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Can intelligence optimise Digital Ecosystems? How could a distributed intelligence interact with the ecosystem dynamics? Can the software components that are part of genetic selection be intelligent in themselves, as in an adaptive technology? We consider the effect of a distributed intelligence mechanism on the evolutionary and ecological dynamics of our Digital Ecosystem, which is the digital counterpart of a biological ecosystem for evolving software services in a distributed network. We investigate Neural Networks and Support Vector Machine for the learning based pattern recognition functionality of our distributed intelligence. Simulation results imply that the Digital Ecosystem performs better with the application of a distributed intelligence, marginally more effectively when powered by Support Vector Machine than Neural Networks, and suggest that it can contribute to optimising the operation of our Digital Ecosystem.
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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 pr
oblems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. Here, we discuss how different computing technologies can contribute to providing the necessary self-organising features, including Multi-Agent Systems, Service-Oriented Architectures, and distributed evolutionary computing. The potential for exploiting these properties in digital ecosystems is considered, suggesting how several key features of biological ecosystems can be exploited in Digital Ecosystems, and discussing 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, considering the self-organised diversity of its evolving agent populations relative to the user request behaviour.
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 pr
oblems. 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).
We view Digital Ecosystems to be the digital counterparts of biological ecosystems, exploiting the self-organising properties of biological ecosystems, which are considered to be robust, self-organising and scalable architectures that can automatical
ly solve complex, dynamic problems. Digital Ecosystems are a novel optimisation technique where the optimisation works at two levels: a first optimisation, migration of agents (representing services) 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 created an Ecosystem-Oriented Architecture of Digital Ecosystems by extending Service-Oriented Architectures with distributed evolutionary computing, allowing services to recombine and evolve over time, constantly seeking to improve their effectiveness for the user base. Individuals within our Digital Ecosystem will be applications (groups of services), created in response to user requests by using evolutionary optimisation to aggregate the services. These individuals will migrate through the Digital Ecosystem and adapt to find niches where they are useful in fulfilling other user requests for applications. Simulation results imply that the Digital Ecosystem performs better at large scales than a comparable Service-Oriented Architecture, suggesting that incorporating ideas from theoretical ecology can contribute to useful self-organising properties in digital ecosystems.
Stability is perhaps one of the most desirable features of any engineered system, given the importance of being able to predict its response to various environmental conditions prior to actual deployment. Engineered systems are becoming ever more com
plex, approaching the same levels of biological ecosystems, and so their stability becomes ever more important, but taking on more and more differential dynamics can make stability an ever more elusive property. The Chli-DeWilde definition of stability views a Multi-Agent System as a discrete time Markov chain with potentially unknown transition probabilities. With a Multi-Agent System being considered stable when its state, a stochastic process, has converged to an equilibrium distribution, because stability of a system can be understood intuitively as exhibiting bounded behaviour. We investigate an extension to include Multi-Agent Systems with evolutionary dynamics, focusing on the evolving agent populations of our Digital Ecosystem. We then built upon this to construct an entropy-based definition for the degree of instability (entropy of the limit probabilities), which was later used to perform a stability analysis. The Digital Ecosystem is considered to investigate the stability of an evolving agent population through simulations, for which the results were consistent with the original Chli-DeWilde definition.
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 pr
oblems. 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 organisms 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.
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