Slay the Spire is a complex deck-building and roguelike game with many possibilities of improving players ability to win. An important part of Slay the Spire is choosing a path that makes the players character as successful as possible. In this study we show that machine learning can help players pick better paths by creating an Artificial Neural Network (ANN) that predicts the most successful path of all available paths, we also discuss what makes a path successful. This study performed two experiments, one user study and one simulation experiment, with the intention of evaluating the created ANN and analysing what makes paths successful. Through the user study this paper shows that the ANN was effective at predicting paths, outperforming all other human players who played normally in all three cases. This study concludes that machine learning can be used effectively to help make pathing decisions in Slay the Spire. Furthermore the study proves the importance of the room types ’Elite’ and ’Campfires’ through the simulation experiment, user study and analysis of data from previous playthroughs.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-43386 |
| Date | January 2021 |
| Creators | Porenius, Oscar, Hansson, Nils |
| 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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