HAMLET: Hyperadaptive Agent-based Modeling for Live Embodied Theatrics (2507.15518v1)

Abstract: Creating an immersive and interactive theatrical experience is a long-term goal in the field of interactive narrative. The emergence of LLM is providing a new path to achieve this goal. However, existing LLM-based drama generation methods often result in AI agents that lack initiative and cannot interact with the physical environment. 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, 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. Our code, dataset and models are available at https://github.com/HAMLET-2025/HAMLET.

• The paper introduces a dual-stage drama generation framework that integrates offline narrative planning with online adaptive performance.
• It employs autonomous AI agents and a PAD module to simulate human-like decision-making and dynamic improvisation.
• Evaluation shows enhanced narrative quality, role consistency, and seamless interaction in live theatrical performances.

HAMLET: Hyperadaptive Agent-based Modeling for Live Embodied Theatrics

The paper "HAMLET: Hyperadaptive Agent-based Modeling for Live Embodied Theatrics" presents a scalable and interactive drama generation framework designed to address key challenges in AI-driven theatrical performances by integrating structured narrative and autonomous agent interactions. The framework aims to enhance both drama generation and live performance quality through the adoption of a multi-agent system and innovative modules like Perceive And Decide (PAD).

Introduction to HAMLET Framework

HAMLET is structured into two main stages: offline planning and online performance. The offline planning stage generates a narrative blueprint that acts as a guiding structure for the live enactment. Here, agents like the actor designer, plot designer, reviewer, and director collaborate to establish character profiles and plot structures. During online performance, each AI actor is endowed with autonomous decision-making abilities, allowing for dynamic improvisation driven by agentic AI principles.

Figure 1: The HAMLET framework creates AI drama in two main stages. First, during offline planning, a collaborative workflow of agents including the actor designer, plot designer, and reviewer creates initial materials, which are then integrated by a director agent into a structured narrative blueprint. This blueprint then guides the subsequent online performance, where a control system composed of a planner, transfer, and advancer directs a dynamic and improvisational theatrical experience.

Offline Planning

In the offline planning phase, the narrative blueprint is crafted from either a customizable topic or a complete literary work. The agent-based workflow starts with character profile creation, which utilizes external data sources to enrich character development, ensuring detailed personality traits and relational attributes are established and reviewed.

Plot Structuring

Plot generation involves defining dramatic points—each a milestone in the story enabling narrative coherence and tension. Environmental elements, including interactive props and scene definitions, play a crucial role, allowing actors to influence their surroundings contextually and impact other actors' decisions.

Figure 2: An illustration of HAMLET's core components for performance generation: a narrative blueprint that defines the scene, plot and character profiles, and the resulting real-time conversation containing scene descriptions and dialogue.

Online Performance

The narrative blueprint transitions into a dynamic performance environment, accommodating both AI and human players. This phase introduces interaction mechanisms and collaborative agents like the Planner, Transfer, and Advancer to ensure the plot advances smoothly.

Performing Drama

The plot unfolds through acts composed of scenes and points. Scenes offer a physical backdrop, whereas points define narrative goals, achieved via trajectories composed of beats—actions aligned with personal goals and the narrative flag.

Figure 3: An example of the real-time interaction and adjudication loop in the online performance. An actor agent attempts an action or speech, termed a beat, to progress towards the current narrative point. The narrator agent then intercepts this attempt, determines whether it is a success or failure, and provides objective feedback to all participants in the drama.

Environment Interaction

The narrator agent adjudicates physical interactions, ensuring logic and realism. Successful interactions update the environment state and are communicated to participants, maintaining consistency and immersion.

Perceive and Decide Module

The PAD module, inspired by Kahneman's dual-process theory, integrates fast and slow thinking mechanisms to simulate human-like decision-making encouraging natural actor responses. This module processes internal actor states and external stimuli to formulate strategies, enhancing dialogue consistency and emotional expression.

Figure 4: The perceive and decide module processes external stimulus and internal state to determine a response strategy by tool calling.

Evaluation Method

HAMLETJudge, a bespoke critic model, evaluates drama performance across three dimensions: Character Performance, Narrative Quality, and Interaction Experience. The evaluation methodology prioritizes holistic drama assessment, circumventing issues associated with single-turn evaluations.

Experiments and Results

HAMLET's implementation demonstrated its efficacy across extensive evaluations, setting new benchmarks in role consistency, narrative quality, and interaction fluidity. Ablation studies confirmed the framework's robustness and the PAD module's impact on dramatic coherence.

Figure 5: Ablation paper of HAMLET framework design.

Conclusion

HAMLET represents a significant advancement in AI-driven interactive drama, fostering immersive theatrical experiences through a balance of structured narrative and autonomous actor interactions. Future developments may enhance AI agency further, exploring deeper environmental manipulations and multi-agent dynamic interactions.

HAMLET's evaluative successes suggest its potential for broader applications in immersive virtual realities and AI-powered entertainment.