Emerging Technologies Technical Committee

Task Force on Bio-Inspired Self-Organizing Collective Systems

Organizers

  Chair:   Prof. Yan Meng, Stevens Institute of Technology, Hoboken, NJ 07030, USA. (E-mail: yan.meng@stevens.edu)

  Vice-Chair:  Prof. Yaochu Jin, University of Surrey, Guildford, Surrey, UK. (E-mail: yaochu.jin@surrey.ac.uk)

Aim and Scope

Self-organization is one of the most important features observed in social, economic, ecological systems and biological systems.  Self-organizing collective systems are supposed to be able to accomplish complex tasks in a changing environment through local interactions among individual agents and local environment without an external global control. The self-organizing collective systems should exhibit life-like features such as self-reconfiguration, self-repair, self-reproduction, self-development, and context awareness.  However, developing such self-organizing systems, where desired global behaviors can emerge through contextual local interactions among individuals and with the environment, is a very challenging task.

Borrowing ideas from biological systems for developing self-organizing collective systems has become increasingly popular in recent years. For example, swarm intelligence, a novel paradigm for solving complex problems with massively parallel systems, has been inspired by behaviors observed in social insect colonies and flocks of birds. Another self-organizing process in biology is morphogenesis of multi-cellular organisms. Morphogenetic approaches based on computational models of embryogeny to self-organizing collective systems have shown to be very promising. 

One example of collective systems is robotic systems.  Individual robots (in a swarm system) or modules (in a modular robot) are supposed to self-organize themselves to build different morphologies and co-functions to adapt to dynamic environments for different tasks in a distributed manner. Furthermore, collective systems would become more powerful if they can co-evolve their controllers and morphological development automatically to adapt to new environments and new tasks in hand. Morphogenetic Robotics (MR), which employs genetic and cellular mechanisms underlying biological morphogenesis for self-organizing swarm and modular robots, is one fast-growing field in developmental robotics that belongs to bio-inspired self-organizing robotic systems.  

This task force aims to promote research in bio-inspired self-organizing collective systems, which is an emerging research area covering multidisciplinary research topics, such as computer science, electrical engineering, mechanical engineering, cognitive science, developmental and systems biology, evolutionary computing, multi-agent systems, machine learning, complex systems, robotics, etc,. In addition, this task force would like to provide a forum for future research discussion and bring together researchers from academia and industry, and attract new blood from younger students and researchers to this exciting merging research area.  

 The topics explored in this task force include, but are not limited to:

·    Genetic and cellular approaches to self-organization and self-assembly

·    Morphogenesis in multi-agent systems

·    Self-reconfiguration and self-assembly in modular robots

·    Co-evolution of neural controller and body development of collective systems

·    Self-organized and self-repairing multi-agent pattern formation, multi-agent flocking and consensus

·    Self-organized collective construction

·    Swarm intelligence based approaches to collective multi-agent systems

·    Distributed task allocation in collective systems

·    Robustness and evolvability of self-organizing collective systems

·    Real world applications, e.g., cognitive network management, coverage, self-assembly of nanostructures, smart materials, swarm robotics, reconfigurable modular robots, and traffic control

Related Events

·         Workshop on “Bio-inspired Self-Reconfigurable and Self-Adaptive Robotic Systems” at IEEE International Conference on Robotics and Automation, 2011. (ongoing)

·         Special issue on “Computational Modeling of Neural and Brain Development”, IEEE Transaction on Autonomous Mental Development.  (ongoing, to be published in 2011).

·         Y. Jin and Y. Meng, Morphogenetic Robotics.  Springer.  (To be published in early 2011).

·         Y. Meng and Y. Jin (Edited). Bio-Inspired Self-Organizing Robotic Systems, Studies on Complexity Intelligence Book Series, Springer. (To be published in early 2011).

·         Special issue on “Evolving Developmental Systems” on IEEE Transaction on Evolutionary Computation, (CFP, to be published 2010).

·         Special issue on “Evolutionary and Developmental Robotics”. IEEE Computational Intelligence Magazine, vol. 5, issue 3, 2010.

·         Special session on “Bio-inspired Self-organizing Multi-Agent Systems” at WCCI 2010 (CEC 2010).

·         Workshop “Bio-inspired Self-organizing Robotic Systems” at IEEE International Conference on Robotics and Automation 2010.

·         Special issue on “Evolving Gene Regulatory Networks” on BioSystems, vol.98, no.3, 2009.

·         Special session on “Evolving Gene Regulatory Networks” at WCCI 2008. (CEC 2008).

Task Force Committee

·         Prof. Wei-Min Shen, University of Southern California, USA.  

·         Prof. Dario Floreano, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.  

·         Prof. Kasper Stoy, University of Southern Denmark, Denmark.

·         Dr. Thomas Schmickl, University of Graz, Austria.

·         Prof. Juyang Weng, Michigan State University, USA.

·         Dr. Serge Kernbach, Universität Stuttgart, Germany. 

·         Dr. Roderich Gross, The University of Sheffield, UK.

·         Prof. Hiroki Sayama, Binghamton University, USA.

·         Prof. Andrew Adamatzky, University of the West of England, UK.

·         Prof. Jon Timmis, University of York, UK. 

Please send comments to: yan.meng@stevens.edu