Rule-Based AI
In this chapter we're going to study rule-based AI systems. Rule-based AI systems are probably the most widely used AI systems for both real-world and game AI applications. In their simplest form, rule-based systems consist of a set of if-then style rules that are used to make inferences or action decisions. Technically, we've already looked at one form of rule-based system on finite state machines. There we used rules to handle state transitions. We also looked at another type of rule-based system in the previous chapter on fuzzy logic.
In this chapter, we're going to look specifically at rule-based systems that commonly are used in so-called expert systems. Examples of real-world, rule-based expert systems include medical diagnosis, fraud protection, and engineering fault analysis. One advantage of rule-based systems is that they mimic the way people tend to think and reason given a set of known facts and their knowledge about the particular problem domain. Another advantage of this sort of rule-based system is that it is fairly easy to program and manage because the knowledge encoded in the rules is modular and rules can be coded in any order. This gives some flexibility both when coding the system and modifying the system at a later time. These advantages hopefully will become clearer to you as we move through the material in this chapter. Before we get into the details, though, let's discuss a couple of game examples that can use rule-based systems.
Imagine you're writing a real-time strategy simulation game involving the typical technology tree, whereby players must train peasants, build facilities, and harvest resources. An illustrative technology tree is shown .
Figure 1. Example technology tree
What we aim to do here is enable the computer opponent to keep track of the player's current state of technology so that the computer opponent can plan and deploy offensive and defensive resources accordingly. Now, you could cheat and give the computer opponent perfect knowledge of the player's state of technology. However, it would be fair and more realistic to have the computer gain knowledge and make inferences on the state of the player's technology in much the same way that the player will have to so that she can assess the computer opponent's state of technology. Both player and computer will have to send out scouts to collect information and then make inferences given the information as it is received. We can achieve this using a fairly simple rule-based system, as you'll see in this chapter.
Let's consider another example. Say you're writing a martial arts fighting game and you want to give the computer the ability to anticipate the player's next strike so that the computer opponent can make the appropriate countermove, such as a counter strike, a dodge, or a parry. The trick here is to somehow keep track of the player's strike patterns—combinations of kicks and punches—and to learn which strike most likely will follow an observed set of previous strikes. For example, if during the fight the player throws a punch, punch combination, what will the player most likely throw next: a punch, a low kick, or a high kick? We can use a rule-based system to achieve such anticipation. We'll come back to this example in much more detail later in this chapter.
