The workshop is comprised of a group of researchers with diverse yet related interests, overlapping in the area of self-organizing systems and methods for characterizing those systems in ways that may ultimately allow them to be guided toward prespecified goals. Information theory and graph theory are core to many of these methods; quantifying complexity and its sources a common theme.

If interested in participating, send an extended abstract to the email addresses on the workshop web site.  Selected works from the workshop will likely be published in a special journal issue (as has been the case in the past).  More information on the GSO-2010 web site.

guided self-organisation, workshop, artificial life, ai

The name “cyber-physical system” (CPS) was chosen by the NSF and other United States federal agencies for systems that coherently combine computational and physical elements.

The CPS field builds up on knowledge and practical experiences of embedded systems, sensor networks, multi-robot teams, modular/swarm robotics, amorphous computing, programmable materials, evolvable/adaptive hardware, etc., and yet promise to form a unique field.

This Workshop will focus on distributed computation in CPS – the computation processes that integrate multiple data streams, compress and structure high-dimensional information, synchronise the distributed dynamics, adapt to topological changes within networks, optimise multiple sensorimotor loops, etc.

Several prominent invited speakers from Australia, Spain and USA will present different aspects of this rapidly developing research field.

Anyone interested in participating in the workshop is encouraged to submit a two-page extended abstract by May 16, 2010. Notifications will be sent by June 11, 2010 to all those who will be invited to the workshop. All accepted submissions will be allocated an oral presentation slot. See the Workshop Web Page for details.

cyber-physical systems, workshop, australia, call for papers, Sydney, australasia, sensor networks

Reservoir computing (RC) is a recent paradigm in the field of recurrent neural networks. Networks in RC have a sparsely and randomly connected fixed hidden layer, and only output connections are trained. RC networks have recently received increased attention as a mathematical model for generic neural microcircuits to investigate and explain computations in neocortical columns. Applied to specific tasks, their fixed random connectivity, however, leads to significant variation in performance. Few problem-specific optimization procedures are known, which would be important for engineering applications, but also in order to understand how networks in biology are shaped to be optimally adapted to requirements of their environment. We study a general network initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP). The IP-based learning uses only local learning, and its aim is to improve network performance in a self-organized way. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much more persistent memory than the other methods but are also able to perform highly nonlinear mappings. We also show that IP-based on sigmoid transfer functions is limited concerning the output distributions that can be achieved.

neurophysiology, optimisation, physiological models, recurrent neural nets, unsupervised learning, reservoir computing, echo state networks

icdl, conference, cfp, learning, development, robotics

Submissions connected to the following non-exhaustive list of topics are particularly encouraged:

• new learning paradigms of RNNs such as unsupervised learning or reservoire learning
• biologically plausible methods
• integration of RNNs and symbolic reasoning
• universal approaches for general data structures such as sets or graphs
• methods which address the generalization ability of RNNs
• challenging applications which have the potential to be benchmark problems
• visionary papers concerning the future of RNNs

Deadline for submissions is 18th of July, 2008.

Technorati Tags: neural networks, rnn, journal, cfp, special issue, recurrent neural networks

We invite participation in the conference by researchers in all areas of robotics, automation and mechatronics. At this year’s conference we will be celebrating the 10th Anniversary of ACRA. For more information, please visit the
conference website. Submission of papers deadline is 5 September 2008.

Technorati Tags: cfp, robotics, conference, australia, robots, canberra

The submission deadline is the 1st July, 2008. For details, check http://www.editorialmanager.com/soro/.

Technorati Tags: cfp, springer, journal, robotics

Original papers are solicited in all areas of unconventional computation; typical, but not exclusive, topics are: natural computing including quantum, cellular, molecular, neural, and membrane computing as well as evolutionary paradigms; chaos and dynamical systems based computing; proposals for computations going beyond the Turing model.

Submissions are due on April 14th, 2008. The call for papers and the conference poster are available from the conference homepage.

Technorati Tags: unconventional, evolutionary computing, conference, quantum computing, chaos, cfp, neural computing, dynamical systems

Architectures for teams of robots, Ambient Intelligence, Biologically inspired systems, Control issues in multi-robot systems, Distributed decision making/problem solving, Distributed/cooperative perception, Distributed planning, Distributed task execution, Human and robot interaction, Learning and adaptation in teams of robots, Multi-robot applications in exploration, search and rescue, Mobiligence (Emergence of Intelligence through Mobility), Modular robotics, Network robotics, Performance metrics for robot teams, Reconfigurable robots, Robot societies, Self-organizing robotic systems, Sensor networks, Swarm robotics, Task allocation.

The conference takes place in Tsukuba, Ibaraki, Japan, Nov. 17-19, 2008. Full paper submission is June 30, 2008. For details, check out the web page.

Technorati Tags: CFP, conference, robotics, distributed systems, autonomous robots

One is confronted with the necessity to adapt sensoric properties and/or configuration to a situation or task at hand, discovery of new sensoric modalities, the use of newly added actuators in novel ways, the necessity of reconfiguring computational hardware after being damaged, and much more. What all these requirements have in common is that, in general, there cannot be a full a priori appreciation of the possible scenarios that can occur during the lifetime of the involved hardware and software.

On the other hand, biological systems are capable to tackle such problems on a regular basis. E.g. the recovery of functionality in experiments where sensoric or neural tissues are transplanted to other than the original locations show that biological systems have a powerful potential to reconfigure their “hardware” and “software” to suit the relevant situation. Biologically inspired approaches, e.g. evolutionary and neural methods, as well as self-organization to tackle these challenges, have been increasingly found to be fruitful. Evolutionary sensorics, self-organizing and self-adaptive controllers, neural strategies have all provided new insights, methodologies, towards the achievement of self- and externally modified sensomotoric loops.

Solutions to these problems has an enormous potential and is amongst the most timely challenges: they would allow the construction of robust, cheap autonomous vehicles, sensor/actuator networks consisting of a large number of autonomous sensor/actuator units (’agents’) that interact with each other to obtain the best results. They would open the way to apply novel sensing/actuation materials for the construction of agents because the self-organized
adaptation mechanisms would be able to deal with the novelty.

The submission deadline is 28 Feb 2008. For details, check the web page

Technorati Tags: sensing, processing, actuation, workshop, cfp, NASA, Adaptive systems