Dans un texte franchement intéressant, Next-Gen.biz nous parle de l’intelligence artificielle (l’AI) appliquée aux jeux vidéo. Quelques exemples sont utilisés (FPS et jeux de courses) pour enrober la réflexion qui sous-tend l’article. Parmi ces exemples, on retrouve notamment Unreal Tournament III, cité ci-dessous. Mais puisque des citations ce n'est pas toujours la panacée, je vous conseille l'article en entier.

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 Steve Polge [l’homme derrière l’AI de quake et d’Unreal Tournament chez Epic Games].

“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.”

Puis, Next-Gen cite encore une fois Steve Polge dans sa conclusion, qui nous fait une petite suggestion futuriste (ici traduite) :

« Une AI avec une solide implémentation d’un bon système de reconnaissance vocale et de synthèse comme interface, ainsi qu’une personnalité intéressante et un modèle de motivation pour les NPC, pourrait avoir un gameplay orienté vers la découverte des motivations des alliés et ennemis. »

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