Book Image

Practical Game AI Programming

By : Micael DaGraça
Book Image

Practical Game AI Programming

By: Micael DaGraça

Overview of this book

The book starts with the basics examples of AI for different game genres and directly jumps into defining the probabilities and possibilities of the AI character to determine character movement. Next, you’ll learn how AI characters should behave within the environment created. Moving on, you’ll explore how to work with animations. You’ll also plan and create pruning strategies, and create Theta algorithms to find short and realistic looking game paths. Next, you’ll learn how the AI should behave when there is a lot of characters in the same scene. You'll explore which methods and algorithms, such as possibility maps, Forward Chaining Plan, Rete Algorithm, Pruning Strategies, Wall Distances, and Map Preprocess Implementation should be used on different occasions. You’ll discover how to overcome some limitations, and how to deliver a better experience to the player. By the end of the book, you think differently about AI.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback
Navigation Behavior and Pathfinding
AI Planning and Collision Avoidance


In this chapter, we unveiled how stealth games work and how we can recreate that same system so we can use it in our games. We went from a simple approach to a complex one, allowing us to decide what fits better in the game that we are creating, if it relies heavily on stealth or if we simply need a basic system to make our character detect the player by vision or audio awareness. The features that we have learned in this chapter can also be expanded and used in practically any example that we have created before, amplifying the collision detection, the pathfinding, the decisions, animations, and many more features, turning them from functional to realistic.

The way we create games is constantly updating, every game published brings a new or different method to create something, which is only possible if we are willing to experiment and blend everything we know, adjusting our knowledge to achieve the results we want to even if they look extremely complicated. Sometimes it is just...