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      Whether you know it or not, you own a finite state machine.
      These machines are marvels of the modern age. They slice, they dice, they julienne. They look good in the kitchen or family room. They come in handy portable models. If you put one in your pocket, it will ...
      Um, just kidding. Truthfully, these machines work in modes like walking, running, standing, and more. These activities are called finite states because each performs a specific function, and they are seperate from each other.
      Thus, finite state machine (FSM) is the term for most computer-controlled enemies in video games. Space invaders shoot or fly. Pacman ghosts roam or chase. Doom imps stand or fight. And because of an unimpressive history like this, finite state machine has almost served as a derogatory term.
      You have likely figured out that your bot is a finite state machine; it either stands, walks, or runs. Very simple behavior. The true alternative to FSMs, however, are neural networks, genetic algorithms, and other complicated techniques not feasible for tutorials or deathmatch.
      We have already done work to improve our FSM system, like the fleeing or chasing during the run state. Because the bot makes varying decisions in those instances, he is employing a simple form of fuzzy logic. Thus he becomes a fuzzy state machine (FuSM). We can build on this.

      Bot AI will be limited, no matter what, since deathmatch itself is limited. A player will never have to rest, eat, repair, sleep, build, or whatever. Consequently, if that new bot by a good programmer you recently downloaded doesn't seem all that fun, its because deathmatch doesn't offer him any exciting opportunities.
      That leaves us with two options: brainstorm for new finite sub-states or expand the gameplay of deathmatch. We will do a bit of both.
      First, since we know bots are inherently more simplistic, let us look at the behavior of a human player. What states might he be in that our bot is not? That's a pretty easy question, really.
      The human contestant might get mad when he is fragged, so he might be in a "hunt" mode. Similarly, he may enter this "hunt" state when he picks up a quad or carries the rocket launcher. In this "hunt" mode, he may merely run swiftly though the level, avoiding all other items, in search of the ratbastard that killed him.
      Lucky for us, this "hunt" state is easy to replicate in the bot AI. Drop down to the routine bot_walk(). At the beginning after the bracket, add this:

	if (is_hunting())
		return;

// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
float() is_hunting =
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
{

	if (self.weapon != IT_ROCKET_LAUNCHER)
		return FALSE;

	if (walkmove(self.angles_y, 20) == FALSE)
		movetogoal(20);

	makevectors(self.angles);
	self.flags = self.flags - FL_ONGROUND;
	self.velocity = self.velocity + v_forward * (self.speed * 2);

	return TRUE;
};

      Second, we can give the bot a new state by writing it into the rules of a deathmatch mode. How about this: in our new mode, all the health boxes are removed. The contestants can gradually regain health by "resting," or standing still.
      Let's get the health part out of the way. Define a new variable called .float rest_time at the bottom of defs.qc. Next, open items.qc and scroll down to the routine PlaceItem(), where items are born. Add the following at the beginning:

	if (self.classname == "item_health")
		{
		remove(self);
		return;
		}

	if (time > self.rest_time && self.health < 100)
		{
		self.health = self.health + 1;
		self.rest_time = time + 1;
		}

	if (time > self.search_time || self.enemy.health <= 0)
		{
		self.goalentity = world;
		self.enemy = world;
		self.pausetime = time + 4;
		self.th_stand();
		return;
		}

		if (self.health < 100)
			{
			self.pausetime = time + (100 - self.health);
			self.th_stand();
			return;
			}

		if (self.health < 100 && self.weapon != IT_ROCKET_LAUNCHER)
			{
			self.pausetime = time + (100 - self.health);
			self.th_stand();
			return;
			}