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, ItalySpringer Science & Business Media, 1979 - 137 lappuses “Look 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
Keynote Presentations | 2 |
Immunocomputing and BiologicalInspiration | 12 |
Congestion Control in Ant Like Moving Agent Systems Alexander Scheidler Daniel Merkle and Martin Middendorf | 33 |
ResourceAware Clustering of Wireless Sensor Networks | 45 |
Sensors Actuators and Networks | 59 |
Experiments with BiologicallyInspired Methods | 70 |
Robotics and MultiAgent Systems | 85 |
Local Strategies for Connecting Stations by Small Robotic Networks | 95 |
Measurement of Robot Similarity to Determine the Best Demonstrator | 105 |
Distributed FaultTolerant Robot Control Architecture Based | 115 |
Markus Lessman and Rolf P Würtz | 150 |
Learned Connection Weights | 177 |
Hardware Issues | 199 |
Imitation in Societies of Autonomous Robots | 233 |
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activity adaptive Affordance Networks algorithm amount of pheromone Anastasia Pagnoni ant-based anti-pheromone ants application approach architecture Artificial Immune Systems assignment Bayesian Network behavior biological cells circuit cluster clusterhead Computer Science configuration congestion control cytokine defined distributed drive dynamic emotion engineering environment evaluate evolutionary experiments exploration packet extended heuristic feedback Figure format when citing FPGA function hardware Hartmut Schmeck heuristic IEEE imitation immuno-engineering implemented input inspired interaction learning method MEXI MEXI's migration Mike Hinchey monitoring move node number of agents object optimal output p2p network parameters pattern performance Petri net pheromone position presented problem query message Rammig random relays response robot router routing scenarios search query selection self-organizing collaboration self-stabilizing service station simulation SO-algorithm Springer strategy success ratio Swarm Intelligence symbols tape thermal energy tion Turing Machine unit disk graph University of Paderborn wireless sensor networks