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Computational Intelligence

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The Computer Generation of Explanatory Dialogues

Explanation and Interaction describes the problems and issues involved in generating interactive user-sensitive explanations. It presents a particular computational system that generates tutorial, interactive explanations of how simple electronic circuits work. However, the approaches and ideas in the book can be applied to a wide range of computer applications where complex explanations are provided, such as documentation, advisory, and expert systems.

A Computer Model

The psychologist William James observed that "a native talent for perceiving analogies is ... the leading fact in genius of every order." The centrality and the ubiquity of analogy in creative thought have been noted again and again by scientists, artists, and writers, and understanding and modeling analogical thought have emerged as two of the most important challenges for cognitive science.

Intelligence takes many forms. This exciting study explores the novel insight, based on well-established ethological principles, that animals, humans, and autonomous robots can all be analyzed as multi-task autonomous control systems. Biological adaptive systems, the authors argue, can in fact provide a better understanding of intelligence and rationality than that provided by traditional AI.

New Directions

Classical computationalism—-the view that mental states are computational states—-has come under attack in recent years. Critics claim that in defining computation solely in abstract, syntactic terms, computationalism neglects the real-time, embodied, real-world constraints with which cognitive systems must cope. Instead of abandoning computationalism altogether, however, some researchers are reconsidering it, recognizing that real-world computers, like minds, must deal with issues of embodiment, interaction, physical implementation, and semantics.

Computational modeling plays a central role in cognitive science. This book provides a comprehensive introduction to computational models of human cognition. It covers major approaches and architectures, both neural network and symbolic; major theoretical issues; and specific computational models of a variety of cognitive processes, ranging from low-level (e.g., attention and memory) to higher-level (e.g., language and reasoning). The articles included in the book provide original descriptions of developments in the field.

This wide-ranging collection of essays is inspired by the memory of the cognitive psychologist John Macnamara, whose influential contributions to language and concept acquisition have provided the basis for numerous research programs. The areas covered by the essays include the foundations of language and thought, congnitive and linguistic development, and mathematical approaches to cognition.

The Cognition and Development of Discovery Processes

Einstein said that "the whole of science is nothing more than a refinement of everyday thinking." David Klahr suggests that we now know enough about cognition—and hence about everyday thinking—to advance our understanding of scientific thinking. In this book he sets out to describe the cognitive and developmental processes that have enabled scientists to make the discoveries that comprise the body of information we call "scientific knowledge."

In Mind and Mechanism, Drew McDermott takes a computational approach to the mind-body problem (how it is that a purely physical entity, the brain, can have experiences). He begins by demonstrating the falseness of dualist approaches, which separate the physical and mental realms. He then surveys what has been accomplished in artificial intelligence, clearly differentiating what we know how to build from what we can imagine building.

Since the 1970s the cognitive sciences have offered multidisciplinary ways of understanding the mind and cognition. The MIT Encyclopedia of the Cognitive Sciences (MITECS) is a landmark, comprehensive reference work that represents the methodological and theoretical diversity of this changing field.

An Introduction to Neural Network Modeling of the Hippocampus and Learning

This book is for students and researchers who have a specific interest in learning and memory and want to understand how computational models can be integrated into experimental research on the hippocampus and learning. It emphasizes the function of brain structures as they give rise to behavior, rather than the molecular or neuronal details. It also emphasizes the process of modeling, rather than the mathematical details of the models themselves.

  • Page 3 of 11