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Computing of applied digital ecosystems

Briscoe, G., De Wilde, Philippe (2009) Computing of applied digital ecosystems. In: MEDES '09: Proceedings of the International Conference on Management of Emergent Digital EcoSystems. . pp. 28-35. ACM ISBN 978-1-60558-829-2. (doi:10.1145/1643823.1643830) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:58023)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.1145/1643823.1643830

Abstract

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 (MASs), Service-Oriented Architectures (SOAs), and distributed evolutionary computing (DEC). 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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1145/1643823.1643830
Uncontrolled keywords: Biological ecosystem; Computing technology; Digital ecosystem; Distributed evolutionary computing; Dynamic problem; Evolutionary computing; Key feature; Scalable architectures; Self-organised; Self-organising; Self-organising features, Information services; Multi agent systems; Service oriented architecture (SOA), Ecosystems
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Philippe De Wilde
Date Deposited: 20 Dec 2022 10:11 UTC
Last Modified: 09 Jan 2023 10:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58023 (The current URI for this page, for reference purposes)

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