Let's begin our journey by taking a virtual microscope and looking inside a single AI entity. It can be a Quake enemy, an Age of Empires army, or the creature from Black & White. Understanding the major building blocks will later help you structure and code your systems efficiently.
Fundamentally, AI systems come in two flavors. The first and most common is the agent, which is a virtual character in the game world. These are usually enemies, but can also be nonplaying characters, sidekicks, or even an animated cow in a field. For these kinds of entities, a biological structure must be followed, so we can somehow model their behavior realistically. Thus, these AI systems are structured in a way similar to our brain. It is easy to identify four elements or rules:
Some AIs are simpler than that and override some components. But this global framework covers most of the entities that exist. By changing the nature of each component, different approaches can be implemented.
The second type of AI entity is abstract controllers. Take a strategy game, for example. Who provides the tactical reasoning? Each unit might very well be modeled using the preceding rules, but clearly, a strategy game needs an additional entity that acts like the master controller of the CPU side of the battle. This is not an embodied character but a collection of routines that provide the necessary group dynamics to the overall system. Abstract controllers have a structure quite similar to the one explained earlier, but each subsystem works on a higher level than an individual.
Let's briefly discuss each element of the structure.
Sensing the World
All AIs need to be aware of their surroundings so they can use that information in the reasoning/analysis phase. What is sensed and how largely depends on the type of game you are creating. To understand this, let's compare the individual-level AI for a game like Quake to the abstract controller from Age of Empires.
In Quake, an individual enemy needs to know:
So the model of the world is relatively straightforward. In such a game, the visual system is a gross simplification of the human one. We assume we are seeing the player if he's within a certain range, and we use simple algorithms to test for collisions with the game world. The sensory phase is essential to gathering information that will drive all subsequent analysis.
Now let's take a look at the sensory data used by the master controller in a strategy game, such as Age of Empires:
Notice that these are not simple tests. For example, we need to know the geometry of the whole game world to ensure that the path finding works as expected for all units. In fact, the vast majority of the AI time in such a game is spent in resolving path-finding computations. The rest of the tests are not much easier. Computing the balance of power so we know where the enemy is and his spatial distribution is a complex problem. It is so complex that we will only recompute the solution once every N frames to maintain decent performance.
In many scenarios, sensing the game world is the slowest part of the AI. Analyzing maps and extracting valuable information from raw data is a time-consuming process.
Storing AI data is often complex because the concepts being stored are not straightforward. In an individual level AI, this will be less of a problem. We can store points and orientations and use numeric values to depict the "state" the AI is in. If the character is walking, the state equals one; if he's running, the state equals two; and so on. Now, how do we store more abstract information, such as the balance of power from the previous paragraph? And how about a path? How do we store a path so the character has a mini-map in memory and remembers how to go from A to B? Some of these data structures are nontrivial, and we will often end up with case-by-case solutions, especially when coding a master controller.
The analysis/reasoning core is what people often think about when they talk about AI. It is the part that actually uses the sensory data and the memory to analyze the current configuration and make a decision. Popular methods for such tasks are finite state machines and rule systems, both of which are dealt with in this chapter. Making a decision can be slow or fast depending on the number of alternatives and the sensory data to evaluate. Chess playing is a slow process, whereas moving a character in Quake is really straightforward. Obviously, a character in Quake has a limited range of options (usually, moving in four directions, jumping and shooting), whereas 20 to 50 moves can be made on a chessboard, given an initial configuration.
Luckily, many games require only simple decision-making processes involving a few choices, and great results often come at a relatively low price. As you will soon see, a lot of games praised for their great AI have been built with relatively simple algorithms.
Intelligence, no matter how sophisticated, must permeate actions and responses, so we realize something's going on inside the creature's virtual brain. Thus, it is essential to couple our AI routines with clever action subroutines, so we get the feeling of real intelligence. In fact, many games exaggerate this action system much like in a theater play, so the character's intentions are obvious and personality is conveyed. By doing so, the degree of intelligence sensed by the player can be much higher than the actual complexity of the core AI routines. As an example, recall the Super Mario Bros game. All types of crazy creatures filled the game world, from turtles to lizards and many other creatures. If you separate logic from the actual actions, you'll discover that these AIs were all very similar. They were either simple enemy chasing AIs or choreographed AIs. But by creating "signature" movements for each one of them, personality and perceived intelligence was enhanced.