Abstract
Procedural content generation uses algorithmic techniques to create large amounts of new content for games and thus reduces the cost of production. However, this content generation is typically the same for all players and is not used to personalize and optimize the game for players’ characteristics. Thus, the core of our research is the improvement of procedural content generation through personalization. We plan to achieve personalization by using modern machine learning algorithms to learn the characteristics of the player. These characteristics will be then used as input parameters for procedural content generation algorithms to produce personalized content. We expect that personalized procedural content generation will have a positive effect on the user’s gameplay experience.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
