"Manipulation" refers to a variety of physical changes made to the world around us. Mechanics of Robotic Manipulation addresses one form of robotic manipulation, moving objects, and the various processes involved -- grasping, carrying, pushing, dropping, throwing, and so on. Unlike most books on the subject, it focuses on...

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them.The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe...

A variety of problems m machine learning and digital communication deal with complex but structured natural or artificial systems. Natural patterns mat we wish to automatically classify' are a consequence of a hierarchical causal physical process. Learning about the world m which we live requires mat we extract useful sensor)'...

What do people learn when they do not know that they are learning? Until recently all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing...

In this book, Shimon Ullman focuses on the processes of high-level vision that deal with the interpretation and use of what is seen in the image. In particular, he examines two major problems. The first, object recognition and classification, involves recognizing objects despite large variations in appearance caused by changes in viewing...

Finite-state devices, which include finite-state automata, graphs, and finite-state transducers, are in wide use in many areas of computer science. Recently, there has been a resurgence of the use of finite-state devices in all aspects of computational linguistics, including dictionary encoding, text processing, and speech processing. This...

Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural...

This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks. Individual chapters were commissioned from selected authors to bridge the...

This new edition of the widely read Evolution in Four Dimensions has been revised to reflect the spate of new discoveries in biology since the book was first published in 2005, offering corrections, an updated bibliography, and a substantial new chapter. Eva Jablonka and Marion Lamb's pioneering argument proposes that there is more...