Intelligent Systems for Engineers and Scientists

The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language, it provides a solid grounding across the full spectrum of AI techniques, so that its readers can implement systems in their own domain of interest.

The coverage includes knowledge-based intelligence, computational intelligence (including machine learning), and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), agents, objects, frames, symbolic learning, case-based reasoning, genetic algorithms and other optimization techniques, shallow and deep neural networks, hybrids, and the Lisp, Prolog, and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control.

Fully updated and revised, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, Fourth Edition 

Features:

• A new chapter on deep neural networks, reflecting the growth of machine learning as a key technique for AI
• A new section on the use of Python, which has become the de facto standard programming language for many aspects of AI

The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood.com.

Whether you are building your own intelligent systems, or you simply want to know more about them, this practical AI textbook provides you with detailed and up-to-date guidance.

Table of Contents

Chapter 1: Introduction

Chapter 2: Rule-Based Systems

Chapter 3: Handling Uncertainty: Probability and Fuzzy Logic

Chapter 4: Agents, Objects, and Frames

Chapter 5: Symbolic Learning

Chapter 6: Single-Candidate Optimization Algorithms

Chapter 7: Genetic Algorithms for Optimization

Chapter 8: Shallow Neural Networks

Chapter 9: Deep Neural Networks

Chapter 10: Hybrid Systems

Chapter 11: Al Programming Languages and Tools

Chapter 12: Systems for Interpretation and Diagnosis

Chapter 13: Systems for Design and Selection

Chapter 14: Systems for Planning

Chapter 15: Systems for Control

Chapter 16: The Future of Intelligent Systems

About the Author

Adrian Hopgood is Full Professor of Intelligent Systems and Director of Future & Emerging Technologies at the University of Portsmouth. He is also a visiting professor at the Open University and at Sheffield Hallam University. He is a Chartered Engineer, Chartered IT Professional, Fellow of the BCS (formerly British Computer Society), and a committee member for the BCS Specialist Group on Artificial Intelligence. He has extensive experience in both academia and industry. He has worked at the level of Dean and Pro Vice-Chancellor in four universities in the UK and overseas. He has also enjoyed technical roles with Systems Designers (now part of Hewlett-Packard) and the Telstra Research Laboratories in Australia. His main research interests are in artificial intelligence and its practical applications. He has supervised 19 PhD projects to completion and published more than 100 research articles. –This text refers to an alternate kindle_edition edition.