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

该项目将开发自动生成场景的高级方法，这些场景本质上是激励性的，对用户的行为做出响应，并且在经济和文化的许多领域都有潜在的好处。 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，目前它们是不灵活的，忽视了个人用户的特点和目标。 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学挑战--任何方法的关键组成部分都是动机。 内在动机，涉及执行一项活动，因为它本质上是有趣的，通常与深度学习和创造力有关。 该项目采用了一种结合计算创造力和逻辑编程推理的方法来应对这一挑战。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，并表明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。 在这样做的过程中，它将执行一个新的评估计划，这将产生一个更精细的理解内在动机和学习之间的关系，包括相互关联的类别的参与，代理，并通过比较集成的场景系统的玩家体验和不允许用户的选择，以影响挑战和叙述的结果的估值。 希望这项研究将展示异构体系结构的实用性，使以前不可能的经验，为用户提供广泛的交互式体验。 一个具体的重点是创造一个基于计算机的教育经验，重点是气象变化，使之成为可能，通过这个项目的技术进步，并告知其相关的用户动机和learning.While研究界的研究成果已经在生成叙事和模拟行动取得了一些成功，生成两者一起提出了新的研究问题。 此外，为了满足社会需求，这一代人必须能够以教学目标和其他明确的目标为指导。 这项研究旨在实现两个根本性的进展。 首先，该项目将展示一个成功的场景生成器，表明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。第二，用户研究将支持第一个基于经验的理解叙事和动作响应和变化对用户体验的影响，特别是参与，动机和学习。 通过展示成功的场景生成，该项目将把注意力转向场景，作为教育软件的潜在基本单元，有可能接触到代表性不足的群体，并产生重大的经济效益。

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.