Toward Strategy Identification and Subtask Decomposition In Task Exploration

TL;DR

This paper presents a task explorer pipeline that automatically identifies strategies and subtasks in user task data, enhancing understanding of user behavior for anticipatory human-machine interaction.

Contribution

It introduces a novel pipeline combining clustering, factor analysis, and string edit distance to identify strategies and subtasks in action-based time-series data.

Findings

Successfully identified key global and local strategies.

Encoded user behavior with hierarchical subtask structures.

Pipeline adaptable to various action-based datasets.

Abstract

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.