通过聚类与编辑距离自动识别任务中的策略和子任务。
Toward Strategy Identification and Subtask Decomposition In Task Exploration
- 结合聚类、因子分析与字符串编辑距离，挖掘全局与局部策略。
- 自动识别任务中多种长度的有意义子任务并构建层级结构。
- 适用于任意动作时序数据，助力人机协同理解用户行为。
本研究基于前瞻型人机交互领域，旨在提升机器对用户知识、技能与行为的理解，实现隐式协调。提出一种任务探索流水线，利用聚类技术结合因子分析与字符串编辑距离，自动识别完成任务时使用的全局与局部策略。全局策略指完成任务的通用动作集合，局部策略则描述这些动作在相似组合中的使用序列。同时，流水线能识别出不同长度的有意义子任务。实验表明，该方法可自动识别关键策略，并以层次化子任务结构编码用户操作记录。此外，开发了任务探索应用以便捷审查结果。该流水线可灵活适配任意动作时序数据，所提取的策略与子任务有助于揭示用户认知与行为特征，支持人机协作优化。
原文摘要 · Abstract (English)
This research builds on work in anticipatory human-machine interaction, a subfield of human-machine interaction where machines can facilitate advantageous interactions by anticipating a user's future state. The aim of this research is to further a machine's understanding of user knowledge, skill, and behavior in pursuit of implicit coordination. A task explorer pipeline was developed that uses clustering techniques, paired with factor analysis and string edit distance, to automatically identify key global and local strategies that are used to complete tasks. Global strategies identify generalized sets of actions used to complete tasks, while local strategies identify sequences that used those sets of actions in a similar composition. Additionally, meaningful subtasks of various lengths are identified within the tasks. The task explorer pipeline was able to automatically identify key strategies used to complete tasks and encode user runs with hierarchical subtask structures. In addition, a Task Explorer application was developed to easily review pipeline results. The task explorer pipeline can be easily modified to any action-based time-series data and the identified strategies and subtasks help to inform humans and machines on user knowledge, skill, and behavior.
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