Artificial Intelligence: A Guide to Intelligent Systems, 4th edition

Published by Pearson (17 September 2024) © 2025

  • Michael Negnevitsky School of Electrical Engineering and Computer Science, University of Tasmania

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Title overview

Artificial intelligence plays an increasingly important role in solving complex problems, supporting decision making, and uncovering insights from data. Artificial Intelligence: A Guide to Intelligent Systems introduces the principles behind intelligent systems and explains how they are designed, applied, and evaluated. Written for learners with little or no programming experience, it presents key AI concepts in a clear and accessible way without relying on advanced mathematics. Through a broad exploration of intelligent technologies, learners gain an understanding of how different approaches can be used to address real-world challenges across a variety of domains.

Learners develop the knowledge needed to understand, compare, and select appropriate intelligent system techniques for different problems. The content explores expert systems, fuzzy systems, neural networks, deep learning, evolutionary computation, knowledge engineering, and data mining, helping learners understand both the strengths and limitations of each approach. Practical examples and case studies connect theory to application, while guidance on choosing suitable methods for specific challenges supports problem-solving and decision-making skills. The broad coverage helps learners build confidence in applying AI concepts across a range of technical and business contexts.

Key Features

  • Understand the core principles that underpin intelligent systems and how they support problem solving and decision making.
  • Compare a range of AI approaches, including expert systems, fuzzy systems, neural networks, evolutionary computation, and hybrid intelligent systems.
  • Evaluate which intelligent system techniques are best suited to different types of real-world problems.
  • Develop knowledge of knowledge representation, reasoning, uncertainty management, and knowledge engineering.
  • Explore machine learning, deep learning, reinforcement learning, and image recognition techniques.
  • Learn how semantic networks and web-based knowledge representation support data organisation and retrieval.
  • Apply data mining and knowledge discovery techniques to identify patterns, relationships, and insights from data.
  • Connect theoretical concepts to practical applications through real-world examples and case studies.

New to This Edition

  • New chapter dedicated to deep learning and convolutional neural networks, covering network architectures, convolutional networks, activation functions, batch normalisation, and image recognition.
  • New section on semantic networks and the semantic web, including applications in data management, search, and data integration.
  • New coverage of model-based and model-free reinforcement learning within the discussion of artificial neural networks and machine learning.
  • New introduction to generative AI, including discussion of chatbot technologies such as Alexa, Siri, and ChatGPT.
  • Two new case studies examine image recognition using convolutional neural networks and optimisation using particle swarm optimisation techniques.

Key features

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Table of contents

  • 1. Introduction to Intelligent Systems
  • 1.1 Intelligent Machines, or What Machines Can Do
  • 1.2 The History of Artificial Intelligence, or From the 'Dark Ages' to Knowledge-based Systems
  • 1.3 Generative AI
  • 2. Expert Systems
  • 2.1 Introduction, or Knowledge Representation Using Rules
  • 2.2 The Main Players in the Expert System Development Team
  • 2.3 Structure of a Rule-based Expert System
  • 2.4 Fundamental characteristics of an expert system
  • 2.5 Forward Chaining and Backward Chaining Inference Techniques
  • 2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System
  • 2.7 Conflict Resolution
  • 2.8 Uncertainty Management in Rule-based Expert Systems
  • 2.9 Advantages and Disadvantages of Rule-based Expert systems
  • 3. Fuzzy Systems
  • 3.1 Introduction, or What Is Fuzzy Thinking?
  • 3.2 Fuzzy Sets
  • 3.3 Linguistic Variables and Hedges
  • 3.4 Operations of Fuzzy Sets
  • 3.6 Fuzzy Inference
  • 3.7 Building a Fuzzy Expert System
  • 4. Frame-based Systems and Semantic Networks
  • 4.1 Introduction, or What Is a Frame?
  • 4.2 Frames as a Knowledge Representation Technique
  • 4.3 Inheritance in Frame-based Systems
  • 4.4 Methods and Demons
  • 4.5 Interaction of Frames and Rules
  • 4.6 Buy Smart: A Frame-based Expert System
  • 4.7 The Web of Data
  • 4.8 RDF - Resource Description Framework and RDF Triples
  • 4.9 Turtle, RDF Schema and OWL
  • 4.10 Querying the Semantic Web with SPARQL
  • 5. Artificial Neural Networks
  • 5.1 Introduction, or How the Brain Works
  • 5.2 The Neuron as a Simple Computing Element
  • 5.3 The Perceptron
  • 5.4 Multilayer Neural Networks
  • 5.5 Accelerated Learning in Multilayer Neural Networks
  • 5.6 The Hopfield Network
  • 5.7 Bidirectional Associative Memory
  • 5.8 Self-organising Neural Networks
  • 5.9 Reinforcement Learning
  • 6. Deep Learning and Convolutional Neural Networks
  • 6.1 Introduction, or How 'Deep' Is a Deep Neural Network?
  • 6.2 Image Recognition or How Machines See the World
  • 6.3 Convolution in Machine Learning
  • 6.4 Activation Functions in Deep Neural Networks
  • 6.5 Convolutional Neural Networks
  • 6.6 Back-propagation Learning in Convolutional Networks
  • 6.7 Batch Normalisation
  • 7. Evolutionary Computation
  • 7.1 Introduction, or Can Evolution Be Intelligent?
  • 7.2 Simulation of Natural Evolution
  • 7.3 Genetic Algorithms
  • 7.4 Why Genetic Algorithms Work
  • 7.5 Maintenance Scheduling with Genetic Algorithms
  • 7.6 Genetic Programming
  • 7.7 Evolution Strategies
  • 7.8 Ant Colony Optimisation
  • 7.9 Particle Swarm Optimisation
  • 8. Hybrid Intelligent Systems
  • 8.1 Introduction, or How to Combine German Mechanics with Italian Love
  • 8.2 Neural Expert Systems
  • 8.3 Neuro-Fuzzy Systems
  • 8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System
  • 8.5 Evolutionary Neural Networks
  • 8.6 Fuzzy Evolutionary Systems
  • 9. Knowledge Engineering
  • 9.1 Introduction, or What Is Knowledge Engineering?
  • 9.2 Will an Expert System Work for My Problem?
  • 9.3 Will a Fuzzy Expert System Work for My Problem?
  • 9.4 Will a Neural Network Work for My Problem?
  • 9.5 Will a Deep Neural Network Work for My Problem?
  • 9.6 Will Genetic Algorithms Work for My Problem?
  • 9.7 Will Particle Swarm Optimisation Work for My Problem?
  • 9.8 Will a Hybrid Intelligent System Work for My Problem?
  • 10. Data Mining and Knowledge Discovery
  • 10.1 Introduction, or What Is Data Mining?
  • 10.2 Statistical Methods and Data Visualisation
  • 10.3 Principal Components Analysis
  • 10.4 Relational Databases and Database Queries
  • 10.5 The Data Warehouse and Multidimensional Data Analysis
  • 10.6 Decision Trees
  • 10.7 Association Rules and Market Basket Analysis
  • Glossary
  • Index

Author bios

Dr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. This text has been developed from his lectures to undergraduates. Educated as an electrical engineer, Dr Negnevitsky's many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control, and environmental engineering. He has authored and co-authored over 300 research publications including numerous journal articles, four patents for inventions, and two books.

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