Teaching AI to play Tabletop Roleplaying Games (TRPG) is a difficult challenge due their negotiable rules and open-ended nature. This is further exacerbated when considering the act of roleplaying, where players do not play or act as themselves, but as a character in a fantasy setting. Previous studies attempting to teach LLMs to play TRPGs do not explicitly discuss role-play in their work, highlighting an absence of a definition in current research. This thesis endeavours in introducing role-playing to AI through developing a prompting method called Control Chain of Thoughts, aimed at teaching it the Dungeons and Dragons alignment system. The prompting method is evaluated through an ablation study where GPT-3.5 is tasked to guess the alignment of characters based on exctracts from D&D gaming sessions. The results indicate a small improvement in GPT’s predictions. Further work needs to be done to evaluate if its alignments help LLMs understand roleplaying.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-24026 |
| Date | January 2024 |
| Creators | Carlander, Deborah |
| Publisher | Högskolan i Skövde, Institutionen för informationsteknologi |
| 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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