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 the 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 to determine the answer 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 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.