Creating an immersive and interactive theatrical experience is a long-term goal in the field of interactive narrative. The emergence of large language model (LLM) is providing a new path to achieve this goal. However, existing LLM-based drama generation methods often result in agents that lack initiative and cannot interact with the physical scene. Furthermore, these methods typically require detailed user input to drive the drama. These limitations reduce the interactivity and immersion of online real-time performance. To address the above challenges, we propose HAMLET, a multi-agent framework focused on drama creation and online performance. Given a simple topic, the framework generates a narrative blueprint, guiding the subsequent improvisational performance. During the online performance, each actor is given an autonomous mind. This means that actors can make independent decisions based on their own background, goals, and emotional state. In addition to conversations with other actors, their decisions can also change the state of scene props through actions such as opening a letter or picking up a weapon. The change is then broadcast to other related actors, updating what they know and care about, which in turn influences their next action. To evaluate the quality of drama performance generated by HAMLET, we designed an evaluation method to assess three primary aspects, including character performance, narrative quality, and interaction experience. The experimental evaluation shows that HAMLET can create expressive and coherent theatrical experiences.

翻译：创造沉浸式、交互式的戏剧体验是交互式叙事领域的长期目标。大语言模型（LLM）的出现为实现这一目标提供了新的路径。然而，现有基于LLM的戏剧生成方法常导致智能体缺乏主动性，且无法与物理场景交互。此外，这些方法通常需要详细的用户输入来驱动剧情发展，这些局限降低了在线实时表演的交互性与沉浸感。为应对上述挑战，我们提出了HAMLET——一个专注于戏剧创作与在线演出的多智能体框架。该框架在给定简单主题后，会生成叙事蓝图以指导后续的即兴表演。在线演出过程中，每位演员被赋予自主意识，这意味着演员可根据自身背景、目标与情感状态独立做出决策。除与其他演员对话外，其决策还能通过拆信、拾取武器等动作改变场景道具的状态。状态变化将广播至其他相关演员，更新其认知与关注点，进而影响其后续行动。为评估HAMLET生成的戏剧演出质量，我们设计了一套评估方法，涵盖角色表现、叙事质量与交互体验三个主要维度。实验评估表明，HAMLET能够创造出表现力丰富且连贯一致的戏剧体验。