Evolutionary and Self-Organizing Sensors, Actuators and Processing Hardware
A special session at AHS-2008: the NASA/ESA Conference on Adaptive Hardware and Systems (June 22-25, 2008, Noordwijk, The Netherlands)
Call for Papers
Recent technology has witnessed the advent of cheap ubiquitous sensing, processing and actuating capabilities for isolated, distributed or collective robotic systems. These appear in the form of intelligent materials, nano-motors and -sensors, Micro-Electro-Mechanical Systems (MEMS), grid processors, Avogadro-scale digital circuits and similar structures. Established conventional AI computation paradigms do not harness the full potential of this new type of technological ability that includes dynamic reconfiguration, addition or removal of sensors, actuators or processing hardware. Classical AI paradigms are inadequate to deal with the requirements of these scenarios which require flexible and adaptive acquisition, manipulation and distribution of information as opposed to sterile off-line AI software designs detached from concrete usage scenarios.
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 proceedings will be published by IEEE.
Submission deadline (for special session papers): 28 Feb 2008.
Daniel Polani, University of Hertfordshire, UK
Chrystopher Nehaniv, University of Hertfordshire, UK
Mikhail Prokopenko, CSIRO, Australia