An Exploration into Computer Games and Computer Generated Forces
John E. Laird
University of Michigan
1101 Beal Ave.
Ann Arbor, Michigan 48109-2110
laird@umich.edu
Keywords:
Computer games, computer generated forces, artificial intelligence, anticipation
ABSTRACT: The artificial intelligence (AI) components of computer games often appear to be very complex, possibly
having abilities beyond the state of the art in computer generated forces. In this paper we study the similarities and
differences between AIs for computer games and computer generated forces (CGFs). We contrast the goals of AIs and
CGFs, their behavioral requirements, and the underlying resources available for developing and fielding them, with an
eye to how they impact the complexity of their behaviors. Our conclusion is that CGFs are currently far ahead of game
AIs, but that this may change soon. We argue that computer games have advantages for doing certain types of research
on complex, human-level behavior. We support this argument with a demonstration of research we have done on AI and
computer games. We have developed the Soar Quakebot, which is a Soar program that plays the death match version of
Quake II. The design of the Soar Quakebot is based on TacAir-Soar, a real-time expert system that flies U.S. military
air missions in simulation, and that is used for training in the U.S. Air Force. The Soar Quakebot incorporates complex
tactics and the ability of the bot to anticipate the actions of its enemy.
1. Introduction
Over the last five years, there have been amazing
advances in the quality and complexity of computer
games. The most noticeable advances have come in
computer graphics, where the number of polygons that are
rendered in a scene seems to increase exponentially each
year. Images on today’s $400 game consoles rival or
surpass those on $50,000 computers from only a few years
ago. Secondary to the graphics has been the complexity of
the underlying environments, be they indoor rooms and
corri