Biologically-Inspired Collaborative Computing: IFIP 20th World Computer Congress, Second IFIP TC 10 International Conference on Biologically-Inspired Collaborative Computing, September 8-9, 2008, Milano, ItalyLook deep into nature and you will understand everything better. advised Albert Einstein. In recent years, the research communities in Computer Science, Engineering, and other disciplines have taken this message to heart, and a relatively new field of biologically-inspired computing has been born. Inspiration is being drawn from nature, from the behaviors of colonies of ants, of swarms of bees and even the human body. This new paradigm in computing takes many simple autonomous objects or agents and lets them jointly perform a complex task, without having the need for centralized control. In this paradigm, these simple objects interact locally with their environment using simple rules. Applications include optimization algorithms, communications networks, scheduling and decision making, supply-chain management, and robotics, to name just a few. There are many disciplines involved in making such systems work: from artificial intelligence to energy aware systems. Often these disciplines have their own field of focus, have their own conferences, or only deal with specialized s- problems (e.g. swarm intelligence, biologically inspired computation, sensor networks). The Second IFIP Conference on Biologically-Inspired Collaborative Computing aims to bridge this separation of the scientific community and bring together researchers in the fields of Organic Computing, Autonomic Computing, Self-Organizing Systems, Pervasive Computing and related areas. We are very pleased to have two very important keynote presentations: Swarm Robotics: The Coordination of Robots via Swarm Intelligence Principles by Marco Dorigo (Université Libre de Bruxelles, Belgium), of which an abstract is included in this volume. |
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Saturs
unocomputing and BiologicalInspiration | 125 |
n Organic Computing Approach to Sustained | 150 |
nage Segmentation by a Network of Cortical Macrocolumns with | 183 |
ollaboration | 196 |
in Robust Evolution of Digital Hardware | 213 |
boration | 229 |
or Index | 245 |
Bieži izmantoti vārdi un frāzes
action active objects adaptive Affordance Networks algorithm antigen application approach architecture artificial immune system autonomous Bayesian Network BCUs behavior biological biosensor calculated cells circuit cluster clusterhead communication components Computer Science configuration congestion counter cytokine defined detection distance distributed drive emotion energy engineering environment evaluated event evolutionary experiments exploration feedback flits FPGA function hardware heuristic human IEEE imitation immuno-engineering implemented input inspired interaction learning load macrocolumn matrix transpose method metric MEXI MEXI's minicolumn monitoring move neighbors nodes optimal output p2p network packet paper parameters performance Petri Petri nets phase pheromone position presented problem query relays response robot router routing scenarios selection self-organizing collaboration self-stabilizing simulation SO-algorithm Springer stochastic strategy Swarm Intelligence symbols Synthetic Biology tape task threshold Turing Machine unit disk graph vector virtual channels wireless