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

Game states

To understand how to create possibility or probability maps we need to first acknowledge the principle aspect necessary to create them, which is called game states, or simply states. We call game states to the actions that are predetermined throughout different occasions in the game, and those actions can be applied to both the player or to the enemy character. Some examples can be simple behavior, such as run, jump, or attack, and those states can be expanded a little more, for example when the character is in the air and cannot attack or if the character has low magical energy and cannot perform a magic attack. In these cases, the character goes from one state to another or can't perform one if it's doing another.