CAREER: Structured High-Agency Interactive Narratives for Virtual Environments

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。叙事是我们思考，沟通和学习的基础。训练模拟等虚拟环境邀请用户在叙事中扮演一个角色，而系统控制所有其他非玩家角色和环境。互动叙事是教人们如何执行任务和教育人们重要主题的有效工具，但编写互动叙事具有挑战性。大多数都是手工编写的，确保了一个很好的结构，但限制了它们的范围，因为每个选择都必须提前想象。有些环境是逼真的模拟，让用户自由地做各种各样的动作，但设计师很难保证叙述具有必要的内容。该项目将使用人工智能规划算法在游戏和训练模拟中创建运行时的叙述。有计划的叙事可以有手写故事的结构和模拟的自由。该项目将探索快速算法，用于生成叙述以及用户记忆和期望的模型。在项目期间，研究小组将开发虚拟环境，（例如针对警官的虚拟现实降级培训模拟），从角色演变而来-两个人之间的游戏练习到一个完全自动化的虚拟环境中，人工智能为每个玩家个性化交互式叙事。这个项目将交互式叙事框架为控制一个角色的玩家和一个角色之间的即兴练习。体验经理，控制虚拟环境的所有其他元素。这些合作伙伴通过他们选择采取的行动来传达他们的信念，意图，记忆和期望，这是一个嘈杂的渠道，需要推理才能理解。本计画将互动式叙事创作操作化为一个互隐式提问的过程。一个参与者采取的每一个行动都会导致他们的伴侣含蓄地询问他们为什么采取这个行动，或者含蓄地回答之前提出的问题。一方能够更好地回答另一方提出的问题，他们就越接近相互理解。MIQA结合了对多智能体人工智能规划，记忆和期望的认知模型以及自动问答程序的研究，以代表双方的合作伙伴以及他们相互理解的程度。在一个互动的虚拟环境中，参与者将做配对练习，其中一个将作为玩家，另一个作为体验经理。在这个练习中，双方都将回答有关他们的行为以及他们对伴侣行为的看法的问题。他们还将报告他们对叙事结构和玩家代理的看法。这些练习将开始作为人对人的练习，但将通过数据收集和模型改进演变为人对智能代理的练习。研究假设是，智能代理将能够提供高代理，结构化的互动叙事，接近与人类合作伙伴创建的质量。这个假设将使用图灵测试进行评估：玩家能否识别交互式叙述是由人类还是智能代理控制。这些演习将在一个名为卡米洛特的虚拟环境中进行，但研究小组将同时实施相同的程序到一个正在进行的虚拟现实训练模拟，该模拟正在与警官培训专家协商，以教授最佳实践，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Narratives are fundamental to the way we think, communicate, and learn. Virtual environments such as training simulations invite the user to play the role of one character in a narrative, while the system controls all the other non-player characters and the environment. Interactive narratives are effective tools for teaching people how to perform a task and educating people about important topics, but writing interactive narratives is challenging. Most are manually written, ensuring a nice structure but limiting their scope because every choice must be imagined in advance. Some environments are realistic simulations that give users freedom to do a wide variety of actions, but then it is hard for the designer to guarantee the narratives have the necessary content. This project will use artificial intelligence planning algorithms to create narratives at run time in games and training simulations. Planned narratives can have the structure of a hand-written story and the freedom of a simulation. This project will explore fast algorithms for generating narratives as well as models of what users remember and expect. Over the project duration, the research team will develop virtual environments (such as a virtual reality de-escalation training simulation for police officers) that evolves from a role-playing exercise between two people to a fully automated virtual environment where the artificial intelligence personalizes the interactive narrative for each player.This project frames interactive narratives as an improvisational exercise between a player who controls one character and an experience manager who controls all the other elements of a virtual environment. These partners communicate their beliefs, intentions, memories, and expectations via the actions they choose to take, which is a noisy channel that requires inference for understanding. This project operationalizes the interactive narrative creation as a Mutual Implicit Question Answering (MIQA) process. Each action taken by one participant causes their partner to implicitly ask questions about why they took that action and/or implicitly answers questions that were raised earlier. The better one partner can answer questions raised by the other, the closer they are to mutual understanding. MIQA combines research on multi-agent artificial intelligence planning, cognitive models of memory and expectations, and procedures from automated question answering to represent both partners and how well they understand one another. Participants in an interactive virtual environment will do paired exercises where one will act as player and the other as experience manager. During this exercise, both partners will answer questions about their actions and about their perceptions of their partner’s actions. They will also report on their perceptions of the structure of the narrative and the player agency. These exercises will begin as person-to-person exercises but will evolve into person-to-intelligent-agent exercises through data gathering and model refinement. The research hypothesis is that the intelligent agent will be able to provide high-agency, structured interactive narratives that approach the quality of those created with a human partner. This hypothesis will be evaluated using a Turing test: can the player identify whether the interactive narration is controlled by a human or an intelligent agent. These exercises will take place in a virtual environment called Camelot, but the research team will simultaneously implement the same procedures into an ongoing virtual reality training simulation that is being built in consultation with police officer training experts to teach best practices for de-escalating potentially dangerous situations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.