Intelligent Systems for Engineers and Scientists
A Practical Guide to Artificial Intelligence
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
2. Rule-Based Systems
3. Handling Uncertainty: Probability and Fuzzy Logic
4. Agents, Objects, and Frames
5. Symbolic Learning
6. Single-Candidate Optimization Algorithms
7. Genetic Algorithms for Optimization
8. Shallow Neural Networks
9. Deep Neural Networks
10. Hybrid Systems
11. AI Programming Languages and Tools
12. Systems for Interpretation and Diagnosis
13. Systems for Design and Selection
14. Systems for Planning
15. Systems for Control
16. The Future of Intelligent Systems
Dr. Adrian Hopgood is Professor of Intelligent Systems and Director of Future & Emerging Technologies at the University of Portsmouth in the UK. He is a Chartered Engineer, Chartered IT Professional, Fellow of the BCS (British Computer Society, the Chartered Institute for IT), and a committee member for the BCS Specialist Group on Artificial Intelligence. He earned his BSc from the University of Bristol, PhD from the University of Oxford, and MBA from the Open University. After completing his PhD, he joined the AI team of Systems Designers PLC. That experience set the direction of his career toward the investigation of intelligent systems and their practical applications. After leaving Systems Designers, he spent 14 years at the Open University and remains attached as a visiting professor. During that period, he also spent two years at Telstra Research Laboratories in Australia, investigating the role of intelligent systems in telecommunications. He has subsequently worked for Nottingham Trent University, De Montfort University, Sheffield Hallam University, and the University of Liège, before joining the University of Portsmouth. Despite assuming several senior management positions during his career, he has never lost his passion for intelligent systems. He has supervised 19 PhD projects to completion and published more than 100 research articles. His website is adrianhopgood.com.
The problem-oriented nature of the book and its pragmatism towards applied AI separates it from many other AI books. I have used the 2nd edition of the book on several aspects of modules taught in Data Analytics. As the new edition has an extended range of topics such as optimization algorithms, neural networks, deep learning, and intelligent agents, it also serves as an excellent and comprehensive reference book on AI topics for applied research.—Dr. Frederic Stahl, Senior Researcher at the German Research Center for Artificial Intelligence (DFKI GmbH)