Evolutionary robotics (ER) aims to apply evolutionary computation techniques to the design of both real and simulated autonomous robots. The Horizons of Evolutionary Robotics offers an authoritative overview of this rapidly developing field, presenting state-of-the-art research by leading scholars. The result is a lively, expansive survey that will be of interest to computer scientists, robotics engineers, neuroscientists, and philosophers.
The contributors discuss incorporating principles from neuroscience into ER; dynamical analysis of evolved agents; constructing appropriate evolutionary pathways; spatial cognition; the coevolution of robot brains and bodies; group behavior; the evolution of communication; translating evolved behavior into design principles; the development of an evolutionary robotics–based methodology for shedding light on neural processes; an incremental approach to complex tasks; and the notion of “mindless intelligence”—complex processes from immune systems to social networks—as a way forward for artificial intelligence.
Contributors Christos Ampatzis, Randall D. Beer, Josh Bongard, Joachim de Greeff, Ezequiel A. Di Paolo, Marco Dorigo, Dario Floreano, Inman Harvey, Sabine Hauert, Phil Husbands, Laurent Keller, Michail Maniadakis, Orazio Miglino, Sara Mitri, Renan Moioli, Stefano Nolfi, Michael O’Shea, Rainer W. Paine, Andy Philippides, Jordan B. Pollack, Michela Ponticorvo, Yoon-Sik Shim, Jun Tani, Vito Trianni, Elio Tuci, Patricia A. Vargas, Eric D. Vaughan
The idea of intelligent machines has become part of popular culture, and t tracing the history of the actual science of machine intelligence reveals a rich network of cross-disciplinary contributions—the unrecognized origins of ideas now central to artificial intelligence, artificial life, cognitive science, and neuroscience. In The Mechanical Mind in History, scientists, artists, historians, and philosophers discuss the multidisciplinary quest to formalize and understand the generation of intelligent behavior in natural and artificial systems as a wholly mechanical process.
The contributions illustrate the diverse and interacting notions that chart the evolution of the idea of the mechanical mind. They describe the mechanized mind as, among other things, an analogue system, an organized suite of chemical interactions, a self-organizing electromechanical device, an automated general-purpose information processor, and an integrated collection of symbol manipulating mechanisms. They investigate the views of pivotal figures that range from Descartes and Heidegger to Alan Turing and Charles Babbage, and they emphasize such frequently overlooked areas as British cybernetic and pre-cybernetic thinkers. The volume concludes with the personal insights of five highly influential figures in the field: John Maynard Smith, John Holland, Oliver Selfridge, Horace Barlow, and Jack Cowan.
Peter Asaro, Horace Barlow, Andy Beckett, Margaret Boden, Jon Bird, Paul Brown, Seth Bullock, Roberto Cordeschi, Jack Cowan, Ezequiel Di Paolo, Hubert Dreyfus, Andrew Hodges, Owen Holland, Jana Horáková, Philip Husbands, Jozef Kelemen, John Maynard Smith, Donald Michie, Oliver Selfridge, Michael Wheeler.
Artificial Life is an interdisciplinary effort to investigate the fundamental properties of living systems through the simulation and synthesis of life-like processes. The young field brings a powerful set of tools to the study of how high-level behavior can arise in systems governed by simple rules of interaction. Some of the fundamental questions include:What are the principles of evolution, learning, and growth that can be understood well enough to simulate as an information process?Can robots be built faster and more cheaply by mimicking biology than by the product design process used for automobiles and airplanes?How can we unify theories from dynamical systems, game theory, evolution, computing, geophysics, and cognition?The field has contributed fundamentally to our understanding of life itself through computer models, and has led to novel solutions to complex real-world problems across high technology and human society. This elite biennial meeting has grown from a small workshop in Santa Fe to a major international conference. This ninth volume of the proceedings of the international A-life conference reflects the growing quality and impact of this interdisciplinary scientific community.
Researchers in artificial life attempt to use the physical representation of lifelike phenomena to understand the organizational principles underlying the dynamics of living systems. The goal of the 1997 European Conference on Artificial Life is to provoke new understandings of the relationships between the natural and the artificial. Topics include self-organization, the origins of life, natural selection, evolutionary computation, neural networks, communication, artificial worlds, software agents, philosophical issues in artificial life, ethical problems, and learning and development.
From Animals to Animats 3 brings together research intended to advance the frontier of an exciting new approach to understanding intelligence. The contributors represent a broad range of interests from artificial intelligence and robotics to ethology and the neurosciences. Unifying these approaches is the notion of "animat" - an artificial animal, either simulated by a computer or embodied in a robot, which must survive and adapt in progressively more challenging environments. The 58 contributions focus particularly on well-defined models, computer simulations, and built robots in order to help characterize and compare various principles and architectures capable of inducing adaptive behavior in real or artificial animals.
- Individual and collective behavior.
- Neural correlates of behavior.
- Perception and motor control.
- Motivation and emotion.
- Action selection and behavioral sequences.
- Ontogeny, learning, and evolution.
- Internal world models and cognitive processes.
- Applied adaptive behavior.
- Autonomous robots.
- Heirarchical and parallel organizations.
- Emergent structures and behaviors.
- Problem solving and planning.
- Goal-directed behavior.
- Neural networks and evolutionary computation.
- Characterization of environments.