The agent schema

Since, during this book, we will be going through the different parts of how an AI agent can perceive, move, plan, and interact with its surroundings, it will be useful to draw a schema for this. Of course, there can be many other schemas, and they are all equally valid, but I believe that this one is particularly useful for getting started with AI Game Development:

Since we chose a bottom-up approach, we should read the schema from the bottom. We will refer to this in more formal terms as our agent model.

First, we can see that the agent always exchanges information with the Game World, which includes geometry, physics, and animations, but also abstractions of it. This information is used at all the levels of our agent model.

From the bottom, we can see that our first concern is how to move in the environment. This is a process that can be split into Motion and Pathfinding (Chapter 3, Navigation). Going up the chain, we can see that the agent perceives the world (Chapter 4, Environment Query System, and 5, Agent Awareness), and, based on this perception, the agent can make a decision (Chapter 2, Behavior Trees and Blackboards). Sometimes, making the best decision at that moment in time, might not lead to a better result in the long run, and so the agent should be able to plan ahead. Often, in video games, an AI system (which is not necessarily an NPC) can control more than one character, and, as a result, it should be able to coordinate a group of characters. Finally, the agent might need to collaborate with other agents. Of course, we won't be able to go deep into each topic in this book, but feel free to take a look online so that you can dive deeper into certain topics.

One final remark: Often, AI in games doesn't use all of these levels at once; some implement just one of them, or mix them up. However, it's important to keep in mind how things are structured before you start playing with them.