Finally, we arrive at fuzzy logic. Put simply, fuzzy logic refers to approximating outcomes as opposed to arriving at binary conclusions. We can use fuzzy logic and reasoning to add yet another layer of authenticity to our AI.
Let's use a generic bad guy soldier in a first person shooter as our agent to illustrate this basic concept. Whether we are using a finite state machine or a behavior tree, our agent needs to make decisions. Should I move to state x, y, or z? Will this task return true or false? Without fuzzy logic, we'd look at a binary value (true or false, or 0 or 1) to determine the answers to those questions. For example, can our soldier see the player? That's a yes/no binary condition. However, if we abstract the decision-making process even further, we can make our soldier behave in much more interesting ways. Once we've determined that our soldier can see the player, the soldier can then "ask" itself whether it has enough ammo to kill the player, or enough health to survive being shot at, or whether there are other allies around it to assist in taking the player down. Suddenly, our AI becomes much more interesting, unpredictable, and more believable.
This added layer of decision making is achieved by using fuzzy logic, which in the simplest terms, boils down to seemingly arbitrary or vague terminology that our wonderfully complex brains can easily assign meaning to, such as "hot" versus "warm" or "cool" versus "cold," converting this to a set of values that a computer can easily understand. In Chapter 7, Using Fuzzy Logic to Make Your AI Seem Alive, we'll dive deeper into how you can use fuzzy logic in your game.
