This book is one of the oldest and most popular introductions to artificial intelligence. An accomplished artificial intelligence (AI) scientist, Winston heads MIT's Artificial Intelligence Laboratory, and his hands-on AI research experience lends authority to what he writes. Winston provides detailed pseudo-code for most of the algorithms discussed, so you will be able to implement and test the algorithms immediately. The book contains exercises to test your knowledge of the subject and helpful introductions and summaries to guide you through the material.

Artificial Intelligence is a somewhat dated introduction to the subject. If you are looking for an introduction to core topics in artificial intelligence (AI), such as logic, knowledge representation, and search, this book has something to offer. However, if you want to learn about some of the newer areas of AI, such as genetic algorithms, neural networks, and intelligent agents, you will wish to select a different text.

Covers cutting edge AI concepts such as neural networks, genetic algorithms, intelligent agents, rules-based systems, ant algorithms, fuzzy logic, unsupervised learning algorithms, and more.

This volume provides a comprehensive introduction to the use of neural networks in mechanical engineering applications. Software implementations for neural networks in C/C++ language and necessary optimization techniques in network training are given in Appendices.

This book is one of the oldest and most popular introductions to artificial intelligence. An accomplished artificial intelligence (AI) scientist, Winston heads MIT's Artificial Intelligence Laboratory, and his hands-on AI research experience lends authority to what he writes. Winston provides detailed pseudo-code for most of the algorithms discussed, so you will be able to implement and test the algorithms immediately. The book contains exercises to test your knowledge of the subject and helpful introductions and summaries to guide you through the material.

Especially suitable for students and researchers in computer science, engineering, and psychology, it provides a systematic development of neural network learning algorithms from a computational perspective.

Provides comprehensive, current, and correct information on problem solving using modern heuristics. Covers classic methods of optimization, including dynamic programming, the simplex method, and gradient techniques, as well as recent innovations such as simulated annealing, tabu search, and evolutionary computation.

Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology.

A text emphasizing both the human and the computer sides of Bayesian networks and decision graphs. Features decision trees, influence diagrams, exercises and examples in part one. Part two deals solely with algorithms and complexity issues.

Topics covered include Scatter Search, Tabu Search, Genetic Algorithms, Genetic Programming, Memetic Algorithms, Variable Neighborhood Search, Guided Local Search, GRASP, Ant Colony Optimization, Simulated Annealing, Iterated Local Search, Multi-Start Methods, Constraint Programming, Constraint Satisfaction, Neural Network Methods for Optimization, Hyper-Heuristics, Parallel Strategies for Metaheuristics, Metaheuristic Class Libraries, and A-Teams.

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.

Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents.

Introduces basic ideas in artificial intelligence from the perspective of building intelligent agents, which the authors define as "anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors."

The Fuzzy Systems Handbook, Second Edition provides a hands-on approach to the architecture, function, and operation of fuzzy logic, the fast-growing alternative to binary logic. Intended for anyone wishing to develop fuzzy systems in a wide range of business, public policy, government, and research applications, the second edition provides not only the software tools, but also a step-by-step approach to designing, constructing, implementing, and maintaining fuzzy system models. Incorporating additional material and more real-world examples, this revised edition updates the subject of fuzzy logic as it increasingly proves itself to be a powerful technology for designing intelligent systems.

An introduction to genetic algorithms and their application in science and engineering. Genetic algorithms are a new and rapidly expanding field and many new applications are being discovered in a variety of disciplines. Some of their uses include solving difficult design problems in science and engineering, optimizing scheduling in operations research and optimizing decision making and portfolio design in management.

Provides a logical and easy-to-follow presentation of introductory and advanced topics in Neural Network technology. The authors provide numerous examples in C++ for use with most C++ compilers, including Borland and Microsoft. Examples show how to implement neural networks and fuzzy logic for further applications and experimentation.

Presents a formal approach to dealing with agents and agent systems. The practicability of this approach is verified by applying the formal framework to three detailed case studies.

Introductory text in fuzzy logic, for those who have no previous experience with the topic. Aims to demonstrate to the reader how one can apply fuzzy theory to real problems, explaining the basics of the theory itself, and fundamental applications of the theory in modern life.

This book is intended to be used for a first course on Fuzzy Control at the Senior/Graduate levels. It contains a somewhat more mathematical treatment than some books on the market as the authors tend to spend quite a bit of time covering fuzzy sets and logic. This is done intentionally to prepare the student for a more detailed study of the control applications. There will be a considerable market for this book as a reference for practicing engineers who need to understand fuzzy controllers.

The book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs

A text covering every aspect of the artificial intelligence approach to robotics. Also covers all of the material needed to program a robot for applications involving sensing, navigation, planning, and uncertainty. Combines theoretical rigor with a narrative touch.

The book is an interesting and worthwhile contribution of a physicist who is intrigued by the special features of living and evolving systems. But what makes this book remarkable is the intellectual approach that the author advocates for research and, in fact, provides as software.