Game AI and academic AI have different objectives. Academic AI researches try to solve real-world problems and prove a theory without much limitation of resources. Game AI focuses on building NPCs within limited resources that seems to be intelligent to the player. The objective of AI in games is to provide a challenging opponent that makes the game more fun to play.

We learned briefly about the different AI techniques that are widely used in games such as FSMs, sensor and input systems, flocking and crowd behaviors, path following and steering behaviors, AI path finding, navigation meshes, behavior trees, and fuzzy logic.

In the following chapters, we'll look at fun and relevant ways in which you can apply these concepts to make your game more fun. We'll start off right away in Chapter 2, Finite State Machines and You, with our own implementation of an FSM, where we'll dive into the concepts of agents, states, and how they are applied to games.