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  • Book Excerpt: AI for Game Developers

    [09.29.06]
    - Glenn Seeman and David Bourg
  • Feature!
    [The following is an excerpt of Chapter 4 written by David M. Bourg and Glenn Seemann from O'Reilly's AI for Game Developers, ISBN: 0-596-00555-5.]
     
    Chapter 4: Flocking
     
    Often in video games, nonplayer characters must move in cohesive groups rather than independently. Let's consider some examples. Say you're writing an online role-playing game, and just outside the main town is a meadow of sheep. Your sheep would appear more realistic if they were grazing in a flock rather than walking around aimlessly. Perhaps in this same role-playing game is a flock of birds that prey on the game's human inhabitants. Here again, birds that hunt in flocks rather than independently would seem more realistic and pose the challenge to the player of dealing with somewhat cooperating groups of predators. It's not a huge leap of faith to see that you could apply such flocking behavior to giant ants, bees, rats, or sea creatures as well.

    These examples of local fauna moving, grazing, or attacking in herds or flocks might seem like obvious ways in which you can use flocking behavior in games. With that said, you do not need to limit such flocking behavior to fauna and can, in fact, extend it to other nonplayer characters. For example, in a real-time strategy simulation, you can use group movement behavior for nonplayer unit movement. These units can be computer-controlled humans, trolls, orcs, or mechanized vehicles of all sorts. In a combat flight simulation, you can apply such group movement to computer-controlled squadrons of aircraft. In a first-person shooter, computer-controlled enemy or friendly squads can employ such group movement. You even can use variations on basic flocking behavior to simulate crowds of people loitering around a town square, for example.

    In all these examples, the idea is to have the nonplayer characters move cohesively with the illusion of having purpose. This is as opposed to a bunch of units that move about, each with their own agenda and with no semblance of coordinated group movement whatsoever.

    At the heart of such group behavior lie basic flocking algorithms such as the one presented by Craig Reynolds in his 1987 SIGGRAPH paper, “Flocks, Herds, and Schools: A Distributed Behavioral Model.” You can apply the algorithm Reynolds presented in its original form to simulate flocks of birds, fish, or other creatures, or in modified versions to simulate group movement of units, squads, or air squadrons. In this chapter we're going to take a close look at a basic flocking algorithm and show how you can modify it to handle such situations as obstacle avoidance. For generality, we'll use the term units to refer to the individual entities comprising the group—for example, birds, sheep, aircraft, humans, and so on—throughout the remainder of this chapter.

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