In-game Content Generation using Machine Learning:  Raheel Yawar, Flying Sheep Studios, Germany

Adapting gameplay difficulty and content based on player profiles is vital for better retention. The Free-to-play Dreamwork’s TrollhuntersRPG is taken as an example for an in-depth look at how Machine Learning algorithms can be employed to discover player profiles and present profile relevant content. The discussion includes implementation details on how these methods can be crafted and adapted for other game genres or use cases and how they can positively impact player retention.

Primarily the talk deals with a special breed of Machine Learning methods called Recommender Systems that can be used to take the load off the designers and learn from players about their skill level and preferences and use that information to tailor in-game content.

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