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Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions.

Devising models that reliably recognize player goals is a key challenge in creating player-adaptive games. Player goal recognition is the task of automatically recognizing the intent of a player from a sequence of observed player actions in a game environment. In open-world digital games, players often undertake suboptimal and varied sequences of actions to achieve goals, and the high degree of freedom afforded to players makes it challenging to identify sequential patterns that lead toward specific goals. To address these issues, we present a player goal recognition framework that utilizes a fine-tuned T5 language model, which incorporates our novel attention mechanism called Temporal Contrary Attention (TCA). The T5 language model enables the framework to exploit correlations between observations through non-sequential self-attention within input sequences, while TCA enables the framework to learn to eliminate goal hypotheses by considering counterevidence within a temporal window. We evaluate our approach using game trace data collected from 144 players' interactions with an open-world educational game. Specifically, we investigate the predictive capacity of our approach to recognize player goals as well as player plans represented as abstract actions. Results show that our approach outperforms non-linguistic machine learning approaches as well as T5 without TCA. We discuss the implications of these findings for the design and development of player goal recognition models to create player-adaptive games.