In FIFA 23, EA are expanding their machine learning algorithm to new areas of the game.

A growing research community is working towards procedurally generating content for computer games and simulation applications with various player modeling techniques. In this paper, we present a two-step procedural content generation framework to minimize players' frustration and/or boredom according to player feedback and gameplay features. In the first step, we dynamically categorize the player styles based on a simple questionnaire beforehand and the gameplay features. In the second step, two player models (frustration and boredom) are built for each player style category. A ranking algorithm is utilized for player modeling to address two problems inherent in player feedback: inconsistency and inaccuracy. Experiment results on a testbed game show that our framework can generate less boring/frustrating levels with very high probabilities.

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division (Ubisoft, 2016). On the other end we ask them to report their levels of competence, autonomy, relatedness and presence using the in-house designed Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods, based on support vector machines, to infer the mapping between gameplay and the four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the obtained models reach accuracies of near certainty, in particular, from 93% up to 97% on unseen players.

And hey, more CG footage of Beyond Good & Evil 2. With Ubisoft's E3 press conferences, you always know exactly what you're going to get, and yet it's also impressive (to me at least) to watch the machine at work, to watch Ubisoft trot out such a full lineup of experiences every year, without fail. We've rounded up all the trailers from Ubisoft's E3 2018 press conference below, and it's exactly what you'd expect. And damn, it might not be inspiring but on some level I can respect the craft. After a fever dream of an introduction for Just Dance 2019 (featuring a dancing panda), Ubisoft finally moved into something we could care about: Beyond Good & Evil 2. First up, just a stunning CG trailer. Like, good enough that I wish Ubisoft would make a Beyond Good & Evil film. It has some fantastic shots of the world itself--including a faux X-Wing flying through empty space, a ship AI being channeled through a jewel-encrusted skull, and the return of the original game's protagonist Jade.

A growing research community is working towards procedurally generating content for computer games and simulation applications with various player modeling techniques. In this paper, we present a two-step procedural content generation framework to minimize players' frustration and/or boredom according to player feedback and gameplay features. In the first step, we dynamically categorize the player styles based on a simple questionnaire beforehand and the gameplay features. In the second step, two player models (frustration and boredom) are built for each player style category. A ranking algorithm is utilized for player modeling to address two problems inherent in player feedback: inconsistency and inaccuracy. Experiment results on a testbed game show that our framework can generate less boring/frustrating levels with very high probabilities.