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Complex Adaptive Systems

Optimal Layouts for the Shuffle-Exchange Graph and Other Networks

This book solves several mathematical problems in the areas of Very Large Scale Integration (VLSI) and parallel computation. In particular, it describes optimal layouts for the shuffle-exchange graph, one of the best known networks for parallel computation. Attempts to design a shuffle-exchange computer have been hampered in part by the fact that, until now, no good layouts for the shuffle-exchange graph were known.

Speed-independent circuits offer a potential solution to the timing problems of VLSI. In this book David Dill develops and implements a theory for practical automatic verification of these control circuits. He describes a formal model of circuit operation, defines the proper relationship between an implementation and its specification, and constructs a computer program that can check this relationship.

Visual Reconstruction presents a unified and highly original approach to the treatment of continuity in vision. It introduces, analyzes, and illustrates two new concepts. The first—the weak continuity constraint—is a concise, computational formalization of piecewise continuity. It is a mechanism for expressing the expectation that visual quantities such as intensity, surface color, and surface depth vary continuously almost everywhere, but with occasional abrupt changes. The second concept—the graduated nonconvexity algorithm—arises naturally from the first.

Interpreting and Responding to Questions in Context

Proceedings of the Eighth International Conference on Artificial Life

The term "artificial life" describes research into synthetic systems that possess some of the essential properties of life. This interdisciplinary field includes biologists, computer scientists, physicists, chemists, geneticists, and others. Artificial life may be viewed as an attempt to understand high-level behavior from low-level rules—for example, how the simple interactions between ants and their environment lead to complex trail-following behavior.

Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior

The Simulation of Adaptive Behavior Conference brings together researchers from ethology, psychology, ecology, artificial intelligence, artificial life, robotics, computer science, engineering, and related fields to further understanding of the behaviors and underlying mechanisms that allow adaptation and survival in uncertain environments.

The goal of neurotechnology is to confer the performance advantages of animal systems on robotic machines. Biomimetic robots differ from traditional robots in that they are agile, relatively cheap, and able to deal with real-world environments. The engineering of these robots requires a thorough understanding of the biological systems on which they are based, at both the biomechanical and physiological levels.

The effort to explain the imitative abilities of humans and other animals draws on fields as diverse as animal behavior, artificial intelligence, computer science, comparative psychology, neuroscience, primatology, and linguistics. This volume represents a first step toward integrating research from those studying imitation in humans and other animals, and those studying imitation through the construction of computer software and robots.

As computers advance from isolated workstations to linked elements in complex communities of systems and people, cooperation and coordination via intelligent agents become increasingly important. Examples of such communities include the Internet, electronic commerce, health institutions, electricity networks, and digital libraries.

Methods and Applications

Animal-like robots are playing an increasingly important role as a link between the worlds of biology and engineering. The new, multidisciplinary field of biorobotics provides tools for biologists studying animal behavior and testbeds for the study and evaluation of biological algorithms for potential engineering applications. This book focuses on the role of robots as tools for biologists.