New and Updated Features:
New Chapter on Deep Learning: Examines different architectures of deep learning and convolutional neural networks, identifying common features of deep neural networks.
Generative AI: A new section explores generative AI and discusses contemporary chatbots like Alexa, Siri, and ChatGPT.
Semantic Networks: A new section discusses successful applications of the semantic web, including improved data management and enhanced search capabilities.
Reinforcement Learning: Introduces the concepts of model-based and model-free reinforcement learning.
Real-World Case Studies: Two new case studies focus on image recognition using a convolutional neural network and finding the optimum of an unknown function using particle swarm optimisation.

Introduction to the principles and applications of artificial intelligence and intelligent systems. Explains expert systems, fuzzy logic, artificial neural networks, evolutionary computation, knowledge engineering, data mining, reinforcement learning, deep learning, semantic networks, and generative artificial intelligence. Emphasizes practical problem-solving techniques and real-world applications through case studies and examples, making complex AI concepts accessible to students and practitioners.

Understand the Simple Ideas Behind AI
What are the principles behind intelligent systems? How are they built? What are intelligent systems useful for? How do we choose the right tool for the job?

These fundamental questions are clearly answered by Michael Negnevitsky’s Artificial Intelligence: A Guide to Intelligent Systems. Unlike many texts burdened with complex matrix algebra and differential equations, this book demonstrates that the core ideas behind intelligent systems are simple and straightforward.

This text assumes little or no prior programming experience as it expertly tackles topics like:

Expert Systems
Fuzzy Systems
Artificial Neural Networks
Evolutionary Computation
Knowledge Engineering
Data Mining