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
1. Auflage. Softcover version of original hardcover Auflage 2008
ISBN: 978-1-4419-3502-1
Verlag: Springer US

Buch, Englisch, Band 268, 247 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 400 g

Reihe: IFIP Advances in Information and Communication Technology

“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.

Zielgruppe

Research

Autoren/Hrsg.

Hinchey, Mike

Schmeck, Hartmut

Pagnoni, Anastasia

Rammig, Franz J.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten

Inhaltsverzeichnis

Keynote Presentations.- Swarm Robotics: The Coordination of Robots via Swarm Intelligence Principles.- Immuno-engineering.- Inspiration Based on Insect Behaviors.- Heuristics for Uninformed Search Algorithms in Unstructured P2P Networks Inspired by Self-Organizing Social Insect Models.- Congestion Control in Ant Like Moving Agent Systems.- Resource-Aware Clustering of Wireless Sensor Networks Based on Division of Labor in Social Insects.- Sensors, Actuators and Networks.- Self-stabilizing Automata.- Experiments with Biologically-Inspired Methods for Service Assignment in Wireless Sensor Networks.- Robotics and Multi-Agent Systems.- Evolving Collision Avoidance on Autonomous Robots.- Local Strategies for Connecting Stations by Small Robotic Networks.- Measurement of Robot Similarity to Determine the Best Demonstrator for Imitation in a Group of Heterogeneous Robots.- Distributed Fault-Tolerant Robot Control Architecture Based on Organic Computing Principles.- Immunocomputing and Biological-Inspiration.- Intrusion Detection via Artificial Immune System: a Performance-based Approach.- Immuno-repairing of FPGA designs.- An Organic Computing Approach to Sustained Real-time Monitoring.- Applications.- A Case Study in Model-driven Synthetic Biology.- Image Segmentation by a Network of Cortical Macrocolumns with Learned Connection Weights.- Integrating Emotional Competence into Man-Machine Collaboration.- Hardware Issues.- Self-optimized Routing in a Network on-a-Chip.- On Robust Evolution of Digital Hardware.- Collaboration.- A Model of Self-Organizing Collaboration.- Guiding Exploration by Combining Individual Learning and Imitation in Societies of Autonomous Robots.