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, Italy

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Springer 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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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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