CHS: Medium: Collaborative Research: Responsive Generation of Intrinsically Motivating Scenarios

该项目将开发先进的方法来自动生成场景，这些场景具有内在的激励作用，对用户的行为做出反应，并可能在经济和文化的许多领域有益。随着众多现有传播媒体的融合，基于计算机的叙事变得越来越复杂、重要和有影响力。然而，目前它们缺乏灵活性，忽视了个体用户的特点和目标。对于我们许多最紧迫的社会需求——从更有效的教育到应对气候挑战——任何方法的关键组成部分都是动机。内在动机是指因为某项活动本身有趣而进行它，它通常与深度学习和创造力有关。该项目采用了一种方法，结合了从计算创造力和逻辑编程中提取的推理类型来应对这一挑战。该研究将展示第一个成功的场景生成器，动态地结合叙述和模拟动作，并显示生成可以由领域模型指导，为项目利用生成的场景来内在地激励学习和其他活动奠定基础。在此过程中，它将执行一种全新的评估计划，能够更准确地理解内在动机和学习之间的关系，包括通过比较玩家在整合场景系统中的体验（是否允许用户的选择影响挑战和叙述）而产生的粘性、代理和结果评估等相关类别。希望这项研究能够证明异构架构的实用性，为用户在广泛的交互体验中实现以前不可能的体验。一个具体的重点是创造一种以气象变化为重点的基于计算机的教育体验，这是由于该项目的技术进步而成为可能，并根据其与用户动机和学习有关的研究结果提供信息。虽然研究界在生成叙事和模拟动作方面取得了一些成功，但将两者结合起来提出了新的研究问题。此外，为了满足社会需要，这一代人必须能够受到教学目标和其他明确目标的指导。这项研究试图取得两个基本进展。首先，项目将演示一个成功的场景生成器，显示生成可以由领域模型指导，这将使未来的项目能够利用生成的场景来内在地激励学习和其他活动。其次，用户研究将支持第一个基于经验的理解，即叙述和行动响应性以及变化对用户体验的影响，特别是参与度、动机和学习。通过演示成功的场景生成，这个项目将把注意力转向作为教育软件潜在的基本单元的场景，具有达到未被充分代表的群体并产生显著经济效益的潜力。

This project will develop advanced methods for automatically generating scenarios that are intrinsically motivating, responsive to the user's behavior, and potentially beneficial in many spheres of the economy and culture. As numerous existing communication media are merging, computer-based narratives have become increasingly complex, significant, and influential. However, at present they are inflexible and ignore the characteristics and goals of the individual user. For many of our most pressing societal needs - from more effective education to addressing climatology challenges - a key component of any approach is motivation. Intrinsic motivation, which involves performing an activity because it is inherently interesting, is often associated with deep learning and creativity. This project employs an approach that combines types of reasoning drawn from computational creativity and logic programming to meet this challenge.The research will demonstrate the first successful scenario generator, dynamically combining narrative and simulated action, and showing that generation can be guided by domain models, laying the foundation for projects to leverage generated scenarios for intrinsically motivating learning and other activities. In so doing, it will execute a novel evaluation plan that will produce a more refined understanding of the relationship between intrinsic motivation and learning, including the interrelated categories of engagement, agency, and valuation of outcomes by comparing player experience of integrated scenario systems with and without allowing the user's choices to influence the challenges and narrative. It is hoped that this research will demonstrate the utility of heterogeneous architectures for enabling previously-impossible experiences for users in a wide range of interactive experiences. A specific focus is creation of a computer-based educational experience focused on meteorological shifts, made possible by this project's technical advances and informed by its findings related to user motivation and learning.While the research community has had some success in generating both narrative and simulated action, generating both together presents novel research questions. Further, to meet social needs, this generation must be capable of being guided by pedagogical and other explicitly-represented goals. This research seeks to achieve two fundamental advances. First, the project will demonstrate a successful scenario generator, showing that generation can be guided by domain models, which will enable future projects to leverage generated scenarios for intrinsically motivating learning and other activities. Second, user studies will support the first empirically grounded understanding of the effects of narrative and action responsiveness and variation on user experience, particularly engagement, motivation, and learning. By demonstrating successful scenario generation, this project will turn attention to the scenario as a potential fundamental unit for educational software, with the potential to reach underrepresented groups and produce significant economic benefit.