Great storytellers know how to take us on a journey. They direct characters to act -- not necessarily in the most rational way -- but rather in a way that leads to interesting situations, and ultimately creates an impactful experience for audience members looking on. If audience experience is what matters most, then can we help artists and animators *directly* craft such experiences, independent of the concrete character actions needed to evoke those experiences? In this paper, we offer a novel computational framework for such tools. Our key idea is to optimize animations with respect to *simulated* audience members' experiences. To simulate the audience, we borrow an established principle from cognitive science: that human social intuition can be modeled as "inverse planning," the task of inferring an agent's (hidden) goals from its (observed) actions. Building on this model, we treat storytelling as "*inverse* inverse planning," the task of choosing actions to manipulate an inverse planner's inferences. Our framework is grounded in literary theory, naturally capturing many storytelling elements from first principles. We give a series of examples to demonstrate this, with supporting evidence from human subject studies.

翻译：伟大的故事讲述者知道如何带领我们踏上旅程。他们引导角色行动——不一定以最理性的方式——而是以一种能引发有趣情境、最终为观众创造有影响力的体验的方式。如果观众体验最为重要，那么我们能否帮助艺术家和动画师**直接**设计这种体验，而无需考虑唤起这些体验所需的具体角色行动？在本文中，我们为这类工具提出了一种新颖的计算框架。我们的核心思想是：基于**模拟**观众体验来优化动画。为模拟观众，我们借鉴了认知科学中的一个成熟原理：人类的社会直觉可以被建模为"逆向规划"，即从观察到的（显性）行为推断智能体（隐藏）目标的任务。基于这一模型，我们将故事叙述视为"**逆向**逆向规划"，即选择行动以操控逆向规划者推断的任务。我们的框架植根于文学理论，能自然地由基本原理捕捉许多故事叙述要素。我们通过一系列示例进行验证，并辅以来自人类受试者研究的支撑证据。