Automated Reasoning and Adaptation for Assisted Experience Design

辅助体验设计的自动推理和适应

• 批准号: RGPIN-2020-06502
• 负责人: Thue, David
• 金额: $ 1.75万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741257
• 关键词: Automated; Reasoning; Adaptation; Assisted; Experience

Many of our daily experiences are designed by one or more people who have particular goals in mind. These designed experiences could include visiting a museum or city park, following a fitness plan, playing a sport or a video game, and more. Sometimes designers aim to meet people's needs, like recovering from an injury, relaxing, or learning a new set of skills. A valuable experience is one that does what it was designed to in a reasonable amount of time, and without any unwanted side-effects. For example, a valuable training experience might fully train a new employee during their first week, without making them want to quit. While many practicing designers work to make our experiences more valuable (that is, better or faster at doing what they were designed to do), their time is limited, and the number of ways that an experience could be made better is enormous. The goal of this research is to study how practicing experience designers might work together with Artificial Intelligence (AI) systems to make more valuable experiences. One current example of an AI-based system is the writing software Grammarly, which supports a writer as they co-design a text, helping to create a more valuable reading experience. We want to understand how other kinds of experience design, besides writing, can be supported by AI systems. To gain this understanding, we will work with different kinds of practicing designers (like museum curators or game designers) to learn about their design processes and translate them into a machine-understandable form. We will also ask these designers about how their current processes might be improved, and for the most impactful (but still solveable) problems, we will build and test AI systems that help solve them. We will also consider the challenge of managing privacy, since people should generally have control over how and when their personal information is used to build or modify their experiences. We want our AI systems to support designers in making just decisions about how personal data will be used. For Canadians, this work is important because many kinds of common experiences could be improved: imagine having more engaging fitness plans, more effective training programs, or more exciting games. For researchers of AI systems, this work represents a key step toward more general AI systems, since reasoning about experiences is an important part of functioning in the world. Finally, this work will show an important way to make progress in AI research: every step forward will be made toward a goal of benefiting society, with tangible improvements to how experiences are designed.

我们的许多日常经历都是由一个或多个心中有特定目标的人设计的。这些设计的体验可以包括参观博物馆或城市公园，遵循健身计划，参加体育运动或电子游戏等等。有时设计师的目标是满足人们的需求，比如从受伤中恢复、放松或学习一套新技能。有价值的体验是在合理的时间内完成设计任务，并且没有任何不必要的副作用。例如，一个有价值的培训经历可能会在新员工入职的第一周就对他们进行全面培训，而不会让他们想要辞职。虽然许多实践设计师致力于让我们的体验更有价值（即更好或更快地完成他们所设计的内容），但他们的时间是有限的，并且可以让体验变得更好的方法是非常多的。本研究的目标是研究实践体验设计师如何与人工智能（AI）系统一起工作，以创造更有价值的体验。当前基于人工智能系统的一个例子是写作软件Grammarly，它支持作者共同设计文本，帮助创造更有价值的阅读体验。我们想要了解除了写作之外，AI系统如何支持其他类型的体验设计。为了获得这种理解，我们将与不同类型的实践设计师（如博物馆策展人或游戏设计师）合作，了解他们的设计过程并将其转化为机器可理解的形式。我们还将询问这些设计师如何改进他们当前的流程，对于最具影响力（但仍然可以解决）的问题，我们将构建和测试人工智能系统来帮助解决这些问题。我们还将考虑管理隐私的挑战，因为人们通常应该能够控制如何以及何时使用他们的个人信息来构建或修改他们的体验。我们希望我们的人工智能系统能够支持设计师就个人数据的使用方式做出公正的决定。对加拿大人来说，这项工作很重要，因为许多常见的经历都可以得到改善：想象一下，有更有吸引力的健身计划，更有效的训练计划，或者更令人兴奋的游戏。对于人工智能系统的研究人员来说，这项工作是迈向更通用的人工智能系统的关键一步，因为对经验的推理是在世界上运作的重要组成部分。最后，这项工作将为人工智能研究取得进展提供一条重要途径：向前迈出的每一步都将朝着造福社会的目标迈进，对体验的设计方式进行切实的改进。