SCH: Human-Centered Reinforcement Learning for Personalized Nutritional Coaching

SCH：以人为本的强化学习个性化营养指导

• 批准号: 2306690
• 负责人: Lena Mamykina
• 金额: $ 119.85万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306690&HistoricalAwards=false
• 关键词: SCH; Human; Centered; Reinforcement; Learning

Chronic diseases, such as type 2 diabetes, hypertension, and obesity, place an ever-increasing burden on individuals and society at large. Health coaching has emerged as an effective approach to promoting self management. However, there are not enough coaching professionals to accommodate the growing population of individuals with chronic diseases. Conversational agents have the potential to overcome these barriers and make health coaching available to a more diverse population. One promising data-driven approach employs reinforcement learning (RL), a machine-learning approach that learns from past interactions and prescribes sequences of actions for reaching a predetermined goal. However, RL-based dialogs can be perceived as unintuitive to users, and there is a need for new approaches to aligning RL-based conversational agents with human reasoning and expectations. In addition, RL algorithms are opaque and there is a need for new approaches to generating explanations for RL inferences and actions.To address these gaps, this project develops a new approach to providing health coaching with RL-based conversational agents, while at the same time addressing more general challenges of designing human-centered RL-based conversational agents. To achieve these goals, this project includes a user study of health coaching in the context of type 2 diabetes, in which human health coaches will be asked to provide guidance to individuals with type 2 diabetes via text messages. The corpus of dialogs collected during this study provides a foundation for developing data driven computational representation of textual meal descriptions and for the development of a chatbot that uses RL to produce conversational structures appropriate for nutritional coaching. Furthermore, this project uses learned representations of meals to provide individuals with feedback on their nutritional choices and explanations for this feedback. Finally, it integrates the human perspective into the RL policy to generate dialog structures that are perceived as intuitive by humans. The evaluation study examines the impact of the RL-based health coach on individuals’ ability to achieve their nutritional goals as compared to other, non-RL-based coaching techniques. This research is consequential to society at large in several ways. First, conversational interfaces can lower entry barriers for engaging with technological interventions in health and wellness for diverse communities and reduce “intervention-generated inequalities” in health. Furthermore, new techniques for aligning RL with human reasoning and explaining its inferences and choices to users can increase its applicability to a broader set of problems and domains. On a broader level, this research and educational plan take important steps towards further promoting human-centered approaches to data science, machine learning, and artificial intelligence education that can have broader impact on future research in this field.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.

2 型糖尿病、高血压和肥胖等慢性病给个人和整个社会带来了越来越大的负担，健康教练已成为促进自我管理的有效方法，但目前却没有足够的教练专业人员来应对。越来越多的患有慢性病的人有潜力克服这些障碍，并为更多样化的人群提供健康指导，一种有前景的数据驱动方法采用强化学习（RL），这是一种可以学习的机器学习方法。过去的互动并规定了顺序然而，基于强化学习的对话可能被用户认为不直观，因此需要新的方法来使基于强化学习的对话代理与人类推理和期望保持一致。此外，强化学习算法是不透明的。并且需要新的方法来生成 RL 推理和行动的解释。为了解决这些差距，该项目开发了一种新方法，通过基于 RL 的对话代理提供健康指导，同时解决更多问题为了实现这些目标，该项目包括一项 2 型糖尿病背景下的健康指导用户研究，其中人类健康教练将被要求为患有 2 型糖尿病的个人提供指导。本研究期间收集的对话语料库为开发文本膳食描述的数据驱动计算表示以及开发使用 RL 生成适合营养指导的对话结构的聊天机器人奠定了基础。使用学习到的膳食表示最后，它将人类的观点融入到强化学习政策中，以生成人类认为直观的对话结构。评估研究检查了基于强化学习的健康的影响。与其他非基于强化学习的指导技术相比，该研究对个人实现营养目标的能力进行了指导，这项研究在几个方面对整个社会产生了重要影响。首先，对话界面可以降低参与健康技术干预的进入壁垒。和不同社区的健康此外，将强化学习与人类推理相结合并向用户解释其推论和选择的新技术可以在更广泛的层面上提高其对更广泛问题和领域的适用性。计划采取重要步骤，进一步促进以人为中心的数据科学、机器学习和人工智能教育方法，这些方法可以对该领域的未来研究产生更广泛的影响。该奖项是 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。