Skip navigation

Artificial Intelligence

  • Page 2 of 3

Uncertainty is a fundamental and unavoidable feature of daily life; in order to deal with uncertaintly intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it.

Support Vector Machines, Neural Networks, and Fuzzy Logic Models

This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments.

This text covers all the material needed to understand the principles behind the AI approach to robotics and to program an artificially intelligent robot for applications involving sensing, navigation, planning, and uncertainty. Robin Murphy is extremely effective at combining theoretical and practical rigor with a light narrative touch.

Foreword by Michael Arbib

An Introduction

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.

Methods, Models, and Conceptual Issues

An Invitation to Cognitive Science provides a point of entry into the vast realm of cognitive science by treating in depth examples of issues and theories from many subfields. The first three volumes of the series cover Language, Visual Cognition, and Thinking.

Philosophy, Psychology, and Artificial Intelligence
Edited by John Haugeland

Mind design is the endeavor to understand mind (thinking, intellect) in terms of its design (how it is built, how it works). Unlike traditional empirical psychology, it is more oriented toward the "how" than the "what." An experiment in mind design is more likely to be an attempt to build something and make it work—as in artificial intelligence—than to observe or analyze what already exists. Mind design is psychology by reverse engineering.

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.

Thinking

An Invitation to Cognitive Science provides a point of entry into the vast realm of cognitive science, offering selected examples of issues and theories from many of its subfields. All of the volumes in the second edition contain substantially revised and as well as entirely new chapters.

Language

An Invitation to Cognitive Science provides a point of entry into the vast realm of cognitive science, offering selected examples of issues and theories from many of its subfields. All of the volumes in the second edition contain substantially revised and as well as entirely new chapters.

  • Page 2 of 3