CAREER: Plan-based Simulation of Human Story Understanding

职业：基于计划的人类故事理解模拟

• 批准号: 2046294
• 负责人: Rogelio Cardona-Rivera
• 金额: $ 54.44万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046294&HistoricalAwards=false
• 关键词: CAREER; Plan; based; Simulation; Human

Storytelling is a fundamental part of the human experience: we tell stories and interpret stories almost daily to share perspectives, teach one another, and communicate between ourselves in more compelling ways. But while narratives are a form of information to which our minds are predisposed, an open question remains: how do people understand stories? By establishing the foundations for a science of narrative that is focused on prediction, this effort aims to afford (to scientists, technologists, and the broader public) the ability to systematically construct stories that resonate with audiences as one intends. This is useful where we already see the use of narrative, when having a higher degree of predictive control in its design would benefit society: the advancement of personalized learning, rehabilitation therapy and healthcare communication, intelligence analysis, automated news generation, and human-aware artificial intelligence (AI).In AI, inventing systems that can model and explain how we process stories is the long-standing grand challenge of “story understanding.” However, this challenge has been broadly approached with methods that ignore the cognitive processes through which humans understand stories. To elevate narrative design from imprecise practices into a systematic and predictable methodology requires a broad, interdisciplinary, and cognitively-grounded effort to reformulate the foundation for story understanding AI. This project outlines a pathway toward using AI planning to generate narratives that predictably elicit a trajectory of mental effects that shape an individual’s story understanding over time across three key cognitive processes that form its basis: event-based mental model updating, inferencing, and memory. The research team will architect algorithms that predict (1) under what conditions people generate inferences about what they read, key to maintaining them engaged, (2) how story structure helps or hinders updating a person’s mental model of a story relative to human inferencing and memory, and (3) how understanding is mediated by said structure in relation to a person’s experience and skill at processing stories (a presently unanswered question in story psychology). Along the way, the effort will make foundational contributions to AI by developing a formal model of time needed to generate plan-based stories, and re-defining the narrative planning process to simulate how humans iteratively revise their beliefs about a story over the course of its narration. Alongside domain experts and psychologists, the research team will pilot, refine, and evaluate the AI software in two domains: interactive narratives for skills training (integral to education) and public science communication (integral to outreach)—both require the engineering of stories for the purpose of more-effective communication.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.

讲故事是人类经验的一个基本组成部分：我们几乎每天都会讲故事并解释故事，以分享观点、互相教导，并以更引人注目的方式进行相互交流。但是，虽然叙事是我们的大脑倾向于接受的一种信息形式，但仍然存在一个悬而未决的问题：人们如何理解故事？ 通过为专注于预测的叙事科学奠定基础，这项工作旨在为（科学家、技术人员和广大公众）提供系统地构建故事的能力，从而按照预期与观众产生共鸣。当我们已经看到叙事的使用时，当在其设计中具有更高程度的预测控制将使社会受益时，这是有用的：个性化学习、康复治疗和医疗保健沟通、情报分析、自动化新闻生成和人类感知人工智能（AI）的进步。在人工智能中，发明可以建模和解释我们如何处理故事的系统是“故事理解”的长期重大挑战。然而，人们普遍采用忽视人类理解故事的认知过程的方法来应对这一挑战。要将叙事设计从不精确的实践提升为系统性和可预测的方法论，需要进行广泛的、跨学科的、基于认知的努力，以重新构建故事理解人工智能的基础。 该项目概述了使用人工智能规划生成叙事的途径，该叙事可预测地引发心理效应轨迹，从而随着时间的推移，在构成其基础的三个关键认知过程中塑造个人对故事的理解：基于事件的心理模型更新、推理和记忆。研究团队将构建算法来预测：（1）人们在什么条件下对所阅读的内容产生推论，这是保持他们参与的关键；（2）故事结构如何帮助或阻碍更新一个人对故事的心理模型（与人类推理和记忆相关）；（3）所述结构如何根据一个人处理故事的经验和技能来调节理解（故事心理学中目前尚未解答的问题）。在此过程中，这项工作将通过开发生成基于计划的故事所需的正式时间模型，并重新定义叙事规划过程来模拟人类如何在叙事过程中迭代地修改他们对故事的信念，从而为人工智能做出基础性贡献。研究团队将与领域专家和心理学家一起在两个领域试点、完善和评估人工智能软件：用于技能培训的交互式叙述（教育的组成部分）和公共科学传播（外展的组成部分）——两者都需要故事设计以实现更有效的沟通。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持。 标准。

项目成果

Game System Models: Toward Semantic Foundations for Technical Game Analysis, Generation, and Design

游戏系统模型：为技术游戏分析、生成和设计奠定语义基础

• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
• 作者: Cardona-Rivera, Rogelio E.;Zagal, Jose P.;Debus, Michael S.
• 通讯作者: Debus, Michael S.

Re-examining the Planning Basis of Goal-driven Autonomy Problems

重新审视目标驱动自主问题的规划基础

• 发表时间: 2022
• 期刊: Proceedings of the Workshop on Integrated Action and Execution at the 32nd International Conference on Automated Planning and Scheduling
• 作者: Cardona-Rivera, Rogelio E.;Gardone, M.;Peterson, Logan;Hiatt, Laura M.;Roberts, Mark
• 通讯作者: Roberts, Mark

Bronco: A Universal Authoring Language for Controllable Text Generation

Bronco：用于可控文本生成的通用创作语言

• 发表时间: 2022
• 期刊: Proceedings of the International Conference on Interactive Digital Storytelling
• 作者: Knochelmann, Jonas P.;Cardona-Rivera, Rogelio E.
• 通讯作者: Cardona-Rivera, Rogelio E.