[Neversoft co-founder West presents a thought-provoking look at improving the believability of AI opponents in games by upping their use of "intelligent mistakes", in a piece originally written for Game Developer magazine.]
Twenty years ago, I was working on my first commercial game: Steve Davis World Snooker, one of the first snooker/pool games to have an AI opponent. The AI I created was very simple. The computer just picked the highest value ball that could be potted, and then potted it.
Since it knew the precise positions of all the balls, it was very easy for it to pot the ball every time. This was fine for the highest level of difficulty, but for easy mode I simply gave the AI a random angular deviation to the shot.
Toward the end of the project, we got some feedback from the client that the AI was "too good." I was puzzled by this and assumed the person wanted the expert mode to be slightly less accurate. So I changed that. But then I heard complaints about the decreased accuracy, and again that the AI was still too good.
Eventually the clients paid a visit to our offices and tried to demonstrate in person what they meant. It gradually came out that they thought the problem was actually with the "easy" mode.
They liked that the computer missed a lot of shots, but they thought that the positional play was too good. The computer always seemed to be leaving the white ball in a convenient position after its shot, either playing for safety or lining up another ball. They wanted that changed.
The problem was, there was no positional play! The eventual position of the white ball was actually completely random. The AI only calculated where the cue ball should hit the object ball in order to make that object ball go into a pocket.
It then blindly shot the cue ball toward that point with a speed proportional to the distance needed to travel, scaled by the angle, plus some fudge factor. Where the white ball went afterward was never calculated, and it quite often ended up in a pocket.
So why was it a problem? Why did they think the AI was "too good" when it was actually random?
Humans have a tendency to anthropomorphize AI opponents. We think the computer is going through a thought process just like a human would do in a similar situation.
When we see the ball end up in an advantageous position, we think the computer must have intended that to happen.
The effect is magnified here by the computer's ability to pot a ball from any position, so for the computer, all positions are equally advantageous.
Hence, it can pot ball after ball, without having to worry about positional play. Because sinking a ball on every single shot would be impossible for a human, the player assumes that the computer is using positional play.
Is this a design problem or a code problem? To a certain extent it depends on the type of game, and to what extent the AI-controlled opponents are intended to directly represent a human in the same situation as the player.
In a head-to-head game such as pool, chess, or poker, the AI decisions are very much determined at a pure code level. In a one-versus-many game, such as an FPS, there is some expectation that your opponents are generally weaker than you are.
After all, you are generally placed in a situation of being one person against countless hordes of bad guys. Other game genres, particularly racing games, pit you against a field of equal opponents. Here the expectation of realistic AI is somewhere between that of chess and the FPS examples.
The more the computer AI has to mimic the idiosyncrasies of a human player, the more the task falls to the programmer. The vast majority of the AI work in a chess game is handled by programmers. Game designers would focus more on the presentation.
In an FPS, the underlying code is generally vastly simpler than chess AI. There is path finding, some state transitions, some goals, and some basic behaviors.
The majority of the behavioral content is supplied via the game designers, generally with some form of scripting. The designers will also be responsible for coding in actions, goals, and responses that emulate the idiosyncrasies of human behavior.