Artificial Life and its attempt to create life-as-it-could-be has widely studied behavior of animals and artifacts. Already shown in early precursors of life-like artificial systems, e.g. Grey Walter's tortoises, or Valentino Braitenberg's vehicles, Artificial Life research is strongly motivated by the desire to understand and create life-like behavior and (neural) control. Creating life-like behaviour in simulation or robots has increased our understanding of design and evolution of controllers for artificial systems. Despite the interrelationship between behaviour, sensors, and other morphological characteristics of animal systems, the evolution of sensors is rarely the primary aim of scientific investigations. The choice of sensors for robots is often limited by practical or financial constraints, and sensors in simulation are often modeled without strong reference to biological sensors. In natural evolution one finds impressive examples of the principle of exploiting new sensory channels and information they carry. Olfactory, tactile, auditory and visual, but also e.g. electrical and even magnetic senses have evolved in a multitude of variants, often utilizing organs not originally "intended" for the purpose they serve at present. Many biological sensors reach a degree of structural and functional complexity and of efficiency which is envied by engineers creating man-made sensors. Sensors enable animals to survive in dynamic and unstructured environments, to perceive and react appropriately to features in the biotic and abiotic environment, including members of the own species as well as predators and prey. Synthesizing artificial sensors for hardware or software systems suggests a similar approach taken for generating life-like behaviour, namely using evolutionary techniques in order to explore design spaces and generate sensors which are specifically adapted with respect to environmental and other fitness related constraints.
Recent advancements in simulation as well as hardware technology provide increasing means to study sensor evolution. This special issue aims to bring together state-of-the-art research in the field of sensor evolution, addressing both the animal as well as the artifact perspective.
Dr. Kerstin Dautenhahn (Artificial Life)
Adaptive Systems Research Group
Department of Computer Science
University of Hertfordshire
Hatfield Herts AL10 9AB
Electronic submissions are discouraged. If it is not possible to send hardcopies, please contact K.Dautenhahn@herts.ac.uk. Any questions concerning the appropriateness of planned submissions or other queries may be directed to the editors:
University of Hertfordshire
Medical University Lubeck
University of Mainz