CAREER: Plan-based Simulation of Human Story Understanding

点击翻译按钮获取中文摘要

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.