Towards Authoring Open-Ended Behaviors for Narrative Puzzle Games with Large Language Model Support

Tue, 14 Apr 2026 00:00:00 +0000

Designing games with branching story lines, object annotations, scene details, and dialog can be challenging due to the intensive authoring required. We investigate the potential for authoring open-ended behaviors for point-and-click narrative games using GPT-3.5, a large language model. In our approach, we extend a behavior tree scripting system with nodes that query GPT-3.5 to generate object descriptions, conversations with characters, and responses to player actions. GPT-3.5 is used to generate content when it hasn’t been scripted manually and to update game state by asking questions about whether a player’s input achieves a particular game goal. We demonstrate our approach with puzzles based on scenes from an episode of Star Trek Voyager. Our approach aims to blend a specific plot with open-ended story elements while keeping the authoring work minimal. Based on a pilot study of 16 participants and our own testing, we find that the generated responses have high coherency and show signs of humor and novelty, but that utterances could be improved to be more interesting and better support the designer’s intent.

Grishma Jena | Andrea De Lorenzo

Towards Authoring Open-Ended Behaviors for Narrative Puzzle Games with Large Language Model Support