Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games

• URL: http://arxiv.org/abs/2505.12439v1
• Date: Sun, 18 May 2025 14:21:56 GMT
• Title: Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games
• Authors: Jinming Zhang, Yunfei Long,
• Abstract summary: Interactive Fiction games (IF games) are where players interact through natural language commands.<n>This work presents a cognitively inspired framework that guides Large Language Models (LLMs) to learn and play IF games systematically.
• Score: 8.06073345741722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interactive Fiction games (IF games) are where players interact through natural language commands. While recent advances in Artificial Intelligence agents have reignited interest in IF games as a domain for studying decision-making, existing approaches prioritize task-specific performance metrics over human-like comprehension of narrative context and gameplay logic. This work presents a cognitively inspired framework that guides Large Language Models (LLMs) to learn and play IF games systematically. Our proposed **L**earning to **P**lay **L**ike **H**umans (LPLH) framework integrates three key components: (1) structured map building to capture spatial and narrative relationships, (2) action learning to identify context-appropriate commands, and (3) feedback-driven experience analysis to refine decision-making over time. By aligning LLMs-based agents' behavior with narrative intent and commonsense constraints, LPLH moves beyond purely exploratory strategies to deliver more interpretable, human-like performance. Crucially, this approach draws on cognitive science principles to more closely simulate how human players read, interpret, and respond within narrative worlds. As a result, LPLH reframes the IF games challenge as a learning problem for LLMs-based agents, offering a new path toward robust, context-aware gameplay in complex text-based environments.

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