Jean-Daniel Nicoud – författare

No matter what your perspective is, what your goals are, or how experienced you are, Artificial Life research is always a learning experience. The variety of phe­ nomena that the people who gathered in Lausanne reported and discussed for the fifth time since 1991 at the European Conference on Artificial Life (ECAL) has not been programmed, crafted, or assembled by analytic design. It has evolved, emerged, or appeared spontaneously from a process of artificial evolution, se- organisation, or development. Artificial Life is a field where biological and artificial sciences meet and blend together, where the dynamics of biological life are reproduced in the memory of computers, where machines evolve, behave, and communicate like living organ­ isms, where complex life-like entities are synthesised from electronic chromo­ somes and artificial chemistries. The impact of Artificial Life in science, phi­ losophy, and technology is tremendous. Over the years the synthetic approach has established itself as a powerful method for investigating several complex phenomena of life. From a philosophical standpoint, the notion of life and of in­ telligence is continuously reformulated in relation to the dynamics of the system under observation and to the embedding environment, no longer a privilege of carbon-based entities with brains and eyes. At the same time, the possibility of engineering machines and software with life-like properties such as evolvability, self-repair, and self-maintainance is gradually becoming reality, bringing new perspectives in engineering and applications.

This book constitutes the refereed proceedings of the 7th International Conference on Artificial Neural Networks, ICANN'97, held in Lausanne, Switzerland,in October 1997. The 201 revised papers presented were selected from a large number of submissions and give a unique documentation of the state of the art in the area. The papers are organized in parts on coding and learning in biology; cortical maps and receptive fields; learning: theory and applications; signal processing: blind source separation, vector quantization, and self-organization; robotics, autonomous agents, and control; speech, vision and pattern recognition; prediction, forecasting and monitoring; and implementations.