In this research, we investigate how multiple demonstrations of a fixed task can be captured and represented in a workflow graph (W-graph) (Figure 1). The idea is to automatically discover the different means of accomplishing a goal from the interaction traces of multiple users, and to encode these in a graph representation. The graph thus represents diverse understanding of the task, opening up arange of possible applications.
W-graphs encode multiple demonstrations of a fixed task, based on commonalities in the workflows employed by users. Nodes represent semantically similar states across demonstrations. Edges represent alternative workflows for sub-tasks. The width of edges represents the number of distinct workflows between two states.
This work has contributed a conceptual approach for representing the different means by which a fixed goal can be achieved in feature rich software, based on recordings of user demonstrations, and has demonstrated a scalable pipeline for constructing such a representation for 3D modeling software. It has also presented a range of applications that could leverage this representation to support users in improving their skill sets over time. Overall, we see this work as a first step toward enabling a new generation of help and learning systems for feature-rich software, powered by data-driven models of tasks and workflows.