Computing of Applied Digital Ecosystems Export

@article{citeulike:5920495, abstract = {A primary motivation for our research in digital ecosystems is the desire toexploit the self-organising properties of biological ecosystems. Ecosystems arethought to be robust, scalable architectures that can automatically solvecomplex, dynamic problems. However, the computing technologies that contributeto these properties have not been made explicit in digital ecosystems research.Here, we discuss how different computing technologies can contribute toproviding the necessary self-organising features, including Multi-AgentSystems, Service-Oriented Architectures, and distributed evolutionarycomputing. The potential for exploiting these properties in digital ecosystemsis considered, suggesting how several key features of biological ecosystems canbe exploited in Digital Ecosystems, and discussing how mimicking these featuresmay assist in developing robust, scalable self-organising architectures. Anexample architecture, the Digital Ecosystem, is considered in detail. TheDigital Ecosystem is then measured experimentally through simulations,considering the self-organised diversity of its evolving agent populationsrelative to the user request behaviour.}, archivePrefix = {arXiv}, author = {Briscoe, G. and De Wilde, P.}, citeulike-article-id = {5920495}, citeulike-linkout-0 = {http://arxiv.org/abs/0910.0674}, citeulike-linkout-1 = {http://arxiv.org/pdf/0910.0674}, day = {5}, eprint = {0910.0674}, keywords = {compsci, ecology, hardware, metaphor, robustness, simulation}, month = {Oct}, posted-at = {2009-10-10 23:42:35}, priority = {4}, title = {Computing of Applied Digital Ecosystems}, url = {http://arxiv.org/abs/0910.0674}, year = {2009} }

TY - JOUR ID - citeulike:5920495 L3 - citeulike-article-id:5920495 N2 - A primary motivation for our research in digital ecosystems is the desire toexploit the self-organising properties of biological ecosystems. Ecosystems arethought to be robust, scalable architectures that can automatically solvecomplex, dynamic problems. However, the computing technologies that contributeto these properties have not been made explicit in digital ecosystems research.Here, we discuss how different computing technologies can contribute toproviding the necessary self-organising features, including Multi-AgentSystems, Service-Oriented Architectures, and distributed evolutionarycomputing. The potential for exploiting these properties in digital ecosystemsis considered, suggesting how several key features of biological ecosystems canbe exploited in Digital Ecosystems, and discussing how mimicking these featuresmay assist in developing robust, scalable self-organising architectures. Anexample architecture, the Digital Ecosystem, is considered in detail. TheDigital Ecosystem is then measured experimentally through simulations,considering the self-organised diversity of its evolving agent populationsrelative to the user request behaviour. TI - Computing of Applied Digital Ecosystems KW - compsci KW - ecology KW - hardware KW - metaphor KW - robustness KW - simulation AU - Briscoe, G AU - De Wilde, P PY - 2009/Oct/05/ UR - http://arxiv.org/abs/0910.0674 ER -