While "Programming AI by Example" may not match the breadth of content in "AI for Games," it certainly delves into the topics it covers with great depth and clarity. It's also worth noting that it addresses several topics absent from Millington's work, such as integrating scripting systems into our games (to simplify AI algorithm design) and the chapter on goal-driven agents.
The book is filled with good illustrative figures that aid comprehension.
Unlike Millington, the implementations are illustrated not in pseudocode but in C++, which, in my opinion, is a downside because it's not exactly the most intuitive language in the world. However, the explanations are extensive and clear enough that a full understanding of the code is not essential.
I also noticed the emphasis on showing UML diagrams of the class hierarchies of the different implementations. I understand why this is done, and it's not excessive, but it made me smile because it feels somewhat outdated. UML diagrams are not commonly seen in books anymore.
Despite all these signs of the time elapsed since the book was published, the field of AI for games has not advanced so much as to render its content obsolete. The principles it covers remain relevant. If anything, some tools that have emerged later, such as behavior trees or neural learning, are missing.
In conclusion, I found the book excellent. I recommend reading it before Millington's. That way, you can enhance its usefulness as an introductory piece to facilitate later understanding of Millington's work and expand on what Buckland leaves pending. With this approach, I believe you'll cover the best of what has been written so far in the field of AI for games.
