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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 problems. 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., Comment: 8 pages, 10 figures, ACM Management of Emergent Digital EcoSystems (MEDES) 2009

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 automatically 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., Comment: 7 pages, 4 figures, conference

We start with a discussion of the relevant literature, including Nature Inspired Computing as a framework in which to understand this work, and the process of biomimicry to be used in mimicking the necessary biological processes to create Digital Ecosystems. We then consider the relevant theoretical ecology in creating the digital counterpart of a biological ecosystem, including the topological structure of ecosystems, and evolutionary processes within distributed environments. This leads to a discussion of the relevant fields from computer science for the creation of Digital Ecosystems, including evolutionary computing, Multi-Agent Systems, and Service-Oriented Architectures. We then define Ecosystem-Oriented Architectures for the creation of Digital Ecosystems, imbibed with the properties of self-organisation and scalability from biological ecosystems, including a novel form of distributed evolutionary computing., Comment: This has been withdrawn by the author due to an error in using presentation notes in submission

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, conference

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., Comment: 12 pages, 14 figures, conference

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 complex, 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., Comment: 8 pages, 6 figures, ACM Management of Emergent Digital EcoSystems (MEDES) 2009

This paper addresses the role of Rural Systemic Initiatives (RSIs) in linking tribally controlled colleges to systemic reform efforts in rural communities. RSI is a program implemented in 1994 by the National Science Foundation to improve science and mathematics education in rural areas. RSIs were established in regions with low population density where more than 30 percent of school-age children live in poverty. The role of institutions of higher education in these efforts has been in areas of pre-service training to student teachers, leadership in curriculum development efforts, and research. Four RSIs in Indian country are the Alaska Native Rural Education Consortium, the Tribal College RSI, the Navajo Nation RSI, and the UCAN RSI. Associated tribal community colleges and universities are listed for each RSI. Assessment criteria for these projects have centered on "drivers" of reform: implementation of comprehensive standards-based curricula; policy development; resource convergence; broad-based support from parents, policymakers, institutions of higher education, business, and community; student achievement; and equity. The report highlights the efforts of the Alaska RSI, which encompasses over 200 remote, indigenous communities in its 586,000 square miles. The success of this project has evolved from the active participation of Native teachers and scholars, who have stimulated integration of traditional ways of knowing in curriculum development. In addition, projects have been successful in using distance learning strategies through institutions of higher education to link the delivery of services, particularly professional development, to remote communities. Costs and community benefits of RSIs are briefly discussed. (LP)