Clearly, when designers dumb down an AI they now need to be smarter about the way they make it stupid. For a game like Unreal Tournament 3, in which the AI opponents must act as much like human multiplayer opponents as possible, this is particularly important.

“We spent more time working on limiting AI capabilities in human-like ways, such as aiming accuracy or world-state knowledge, than any other AI problem,” says Polge. “Before UT3, the approach we used was to determine the factors that made human players more or less likely to hit a target – like whether the target was stationary or moving, whether its movement was erratic, whether the shooter had just been knocked around by a shot, whether the shooter was stationary or moving – and use these factors to modify the magnitude of the random aiming error.

“This approach worked reasonably well in terms of mimicking how frequently a target should be hit, but it broke down in a couple of ways. The first was that at some extremes, such as [when the target was] very close or very far away, this accuracy model wasn’t as realistic. The second was that bots would miss as frequently as a human, but not in the same way. For example, when a player suddenly dodges to the side, other humans tend to miss by shooting where the player used to be going, rather than with a large spread around where the player is currently going.

“We improved the aiming model significantly in UT3 by adding reaction time to the bot’s model of where a player is going. Rather than extrapolating where a player will be when the projectile reaches their location based on the player’s current location and velocity, bots extrapolate their enemy’s position based on what they were doing a few hundred milliseconds ago – which is what humans do. This results in bot aiming ‘feeling’ much more human-like.”

Similar solutions have been developed to deal with the uncanny skillset of opponents in racing games. ‘Rubber-banding’ has been one way of addressing the issue of creating a consistently surmountable challenge, causing AI drivers to adjust their driving capabilities, or even achieve impossible speeds, in order to tax you regardless of how well or how poorly you are driving. This too has proved unconvincing at times, with considerable leads being improbably reduced in seconds and vice versa.

“Rubber-banding is an interesting artform,” says Hamish Young, a producer at Criterion who has worked on every Burnout game. “Essentially there are some cars in the pack you want to be around however badly the player plays, to encourage them to get back into the race. These are the back-markers. Then there are a group of cars in the middle who stretch between the back-markers and the pacemakers. The pacemakers are the front couple of cars and they in effect set the difficulty of a race. Over the course of the different Burnouts we have added more and more cars to the races, which means the rubber-banding can be more subtly spread across the pack.”

Burnout’s emphasis on battling with other vehicles and forcing them from the road allows for more variables by which the abilities of the AI can be reduced or increased – disguising the degree with which this is contrived to match player skill.

“I think in general in the genre you either get cars that drive almost impossibly well and often they ignore that you exist,” says Young. “In Burnout, neither cliché is true. We try to make our AI behave in a human way mainly by trying to get them to only make the same mistakes a human would. For Burnout, this would be things like mispredicting where a piece of cross-traffic will be and crashing into it. The reasons for misprediction are mostly similar to what a human experiences: there is a degree of guessing where the traffic will be and when you could potentially contact it. Causing AI to crash is relatively easy because you can directly play with its perception – for example, make it ignore a piece of traffic, make it think a corner is wider than it is, etc. Ultimately, it requires understanding the mistakes humans make and why their judgments are off and then building a system whose judgments can be similarly off.”