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

这个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的，对用户的行为做出反应，并可能在经济和文化的许多领域中受益。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--任何方法的一个关键组成部分都是动力。内在动机通常与深度学习和创造力联系在一起，它涉及到执行一项活动，因为它本质上是有趣的。该项目采用了一种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的方法。该研究将展示第一个成功的场景生成器，动态结合叙事和模拟动作，并表明生成可以由域模型指导，为项目利用生成的场景进行内在激励的学习和其他活动奠定基础。在这样做的过程中，它将执行一项新颖的评估计划，通过比较集成场景系统的玩家体验和不允许用户的选择影响挑战和叙事，对内在动机和学习之间的关系产生更精细的理解，包括参与、代理和结果评估等相互关联的类别。希望这项研究将证明异类体系结构在为用户在广泛的交互体验中实现以前不可能的体验方面的效用。一个特别的重点是创造一个以气象变化为重点的基于计算机的教育体验，这个项目的技术进步使之成为可能，并得到了与用户动机和学习相关的发现的启发。虽然研究界在生成叙事和模拟动作方面取得了一些成功，但同时生成两者会带来新的研究问题。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代表的目标为指导。这项研究寻求实现两个根本性的进展。首先，该项目将演示一个成功的场景生成器，展示生成可以由域模型指导，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，用户研究将支持对叙事和动作响应性和变化对用户体验的影响的第一个经验性理解，特别是参与、动机和学习。通过展示成功的情景生成，该项目将把注意力转向情景，将其作为教育软件的潜在基本单位，有可能接触到代表性不足的群体并产生显著的经济效益。

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.