The consumers of today have become used to a constant flow of new content. Whether the content being music, film and series, games or other forms of media. This has created a strain on the developers and creators, to create new and original content for an ever demanding audience. Creating original content can be a costly and time consuming process. Today there are tools to help in this process, many that build on the idea of procedurally generated content. But with these tools alone it can be hard for the creator to leave their mark on the content, since most of it will look all the same from the same tool. We propose CLAPPY, or Collaborative Learning Algorithm for Predicting Personal Yield. The AI colleague that will learn the patterns of the creator, and help them express themselves in their content, regardless of the tool. Controlled experiments were conducted where subjects were given a content creation tool in the form of a terrain generator for games. The thesis then compares the results of the content creation tool when the subjects used it on their own and with an AI-collaborator.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-61692 |
| Date | January 2023 |
| Creators | Larsson, Gustaf, Lindecrantz, Valter |
| Publisher | Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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