Game AI and academic AI have different objectives. Academic AI researchers try to solve real-world problems and prove a theory without much limitation in terms of resources. Game AI focuses on building NPCs within limited resources that seem 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 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, and we'll dive into the concepts of agents and states and how they are applied to games.
