用多智能体框架让角色自主演戏,还能动道具、互动环境。
HAMLET: A Hierarchical and Adaptive Multi-Agent Framework for Live Embodied Theatrics
- 分层自适应多智能体设计,角色按身份决策并记忆剧情。
- 角色能开信、拿武器等动作,实时改变场景状态。
- 专设评价模型自动打分,适合沉浸式戏剧生成研究者。
构建沉浸式互动戏剧体验是交互叙事领域的长期目标。大型语言模型(LLMs)为此提供了新路径。然而,现有戏剧生成方法常导致模型缺乏主动性,无法与物理场景互动,且需详细输入,削弱现场表演的沉浸感。为此,我们提出HAMLET——一种面向戏剧创作与实时在线演出的分层自适应多智能体框架。给定简单主题后,框架首先生成叙事蓝图以引导即兴演出。演出过程中,每个角色智能体配备自适应推理模块,可根据其人物设定、记忆和目标,在复杂群聊中自主决策。除对话外,角色智能体通过打开信件、拾起武器等动作实现具身交互,动作广播更新全局环境状态。为客观评估现场具身戏剧质量,我们建立综合评估方法,并引入专用评判模型HAMLETJudge进行自动化评估。实验表明,HAMLET能自主生成富有表现力、连贯且具备物理交互性的戏剧体验。
原文摘要 · Abstract (English)
Creating an immersive and interactive theatrical experience is a long-term goal in the field of interactive narrative. The emergence of large language models (LLMs) provides a new path to achieve this goal. However, existing drama generation methods often produce LLMs that lack initiative and cannot interact with the physical scene, while typically requiring detailed input that diminishes the immersion of live performance. To address these challenges, we propose HAMLET, a hierarchical adaptive multi-agent framework focused on drama creation and real-time online performance. Given a simple topic, the framework initially generates a narrative blueprint to guide the subsequent improvisational performance. During online performance, each actor is equipped with an adaptive reasoning module that enables decision-making based on their personas, memories, goals during complex group chat scenarios. Beyond dialogue, actor agents engage in embodied interactions by changing the state of scene props through actions such as opening a letter or picking up a weapon, which are broadcast to update the global environmental context. To objectively assess the quality of live embodied theatrics, we establish a comprehensive evaluation method and introduce HAMLETJudge, a specialized critic model for automated evaluation. Experimental results demonstrate that HAMLET excels in creating expressive, coherent, and physically interactive theatrical experiences in an autonomous manner.
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