Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life

Part 1 Autonomous robots: artificial life and real robots, Rodney A. Brooks concept formation as emergent phenomena, Mukesh J. Patel and Uwe Schnepf distributed adaptive control - a paradigm for designing autonomous agents, Rolf Pfeifer and Paul Verschure taking eliminative materialism seriously - a methodology for autonomous systems research, Tim Smithers an adaptable mobile robot, Leslie Pack Kaelbling learning behaviour networks from experience, Pattie Maes characterizing adaptation by constraint, Ian Horswill on the self-organizing properties of topological maps, Didier Keymeulen and Jo Decuyper massively parallel evolution of recurrent networks - an approach to temporal processing, Piet Spiessens and Jan Torreele neural networks for visual tracking in an artificial fly, Dave Cliff an approach to sensorimotor relevance, Eric Dedieu and Emmanuel Mazer using motor actions for location recognition, Ulrich Nehmzow and Tim Smithers the application of temporal difference learning to the neural control of quadruped locomotion, Martin Snaith and Owen Holland evolution of subsumption using genetic programming, John R. Koza. Part 2 Swarm intelligence: warm-made architectures, Jean-Louis Deneubourg, et al distributed optimization by ant colonies, Alberto Colorni, et al emergent colonization in an artificial ecology, Andrew M. Assad and Norman H. Packard the maximum entropy principle and sensing in swarm intelligence, Gerardo Beni and Susan Hackwood a behavioural simulation model for the study of emergent social structures, Alexis Drogoul, et al interactive evolution of dynamical systems, Karl Sims simulating co-evolution with mimetism, Nicolas Meuleau dynamics of artificial markets - speculative markets and emerging "Common Sense" knowledge, Christian Nottola, et al harvesting by a group of robots, S. Goss and J.L. Deneubourg. Part 3 Learning and evolution: learning, behaviour and evolution, Domenico Parisi, et al immune network and adaptive control, Hugues Bersini genetic self-learning, Frank Hoffmeister and Thomas Back Darwin's continent cycle theory and its simulation by the prisoner's dilemma, Heinz Muhlenbein the royal road for genetic algorithms - fitness landscapes and GA performance, Melanie Mitchell, et al using marker-based genetic encoding of neural networks to evolve finite-state behaviour, Brad Fullmer and Risto Miikkulainen self-adaptation in genetic algorithms, Thomas Back steerable GenNets - the genetic programming of steerable behaviours in GenNets, Hugo de Garis an action based neural network for adaptive control - the tank case study, Antonio Rizzo and Neil Burgess a model of formal neural network for non-supervised learning and recognition of temporal sequences, Bruno Gas. (Part contents)