Decomposing and organizing a complex task becomes inducing a graph  Gs(t) = (V, E) from a complex task input t. We assume that the input is complex because distinguishing complex tasks from simple tasks is not the focus of this exercise.

Modeling Overview (Learning to Decompose and Organize Complex Tasks)

These equations formalize "make a flow chart" so that the process can be automated.

University of Rochester

Some researchers have used graph theory to create an end-to-end pipeline that creates a dependency graph from unstructured text. It represents subtasks and their time dependencies on each other. It is a way to automate the decomposition process.

Using Graph Theory to Decompose Tasks

Zhang, Y., Jauhar, S. K., Kiseleva, J., White, R., &amp; Roth, D. (2021). Learning to Decompose and Organize Complex Tasks. ACL Anthology. https://www.aclweb.org/anthology/2021.naacl-main.217/. 

Learning to Decompose and Organize Complex Tasks

1. Can we accurately and automatically generate subtasks given an input complex task?
2. Can we correctly identify the temporal dependencies between subtasks?

Research Questions (Learning to Decompose and Organize Complex Tasks)

1. Task
2. Complex Task
3. Subtask
4. Temporal Dependencies

Terms Related to Modeling Tasks

The model is being treated as a graph induction problem, i.e. how to make a graph given the input. In graph theory, there are two main parts of a graph: nodes (points) and edges (what connects them). Here, nodes represent subtasks. The edges between the nodes represent temporal dependencies, which illustrate which tasks must be completed before another one is actionable. For example, you must create a guest list before you can send out invitations for a party. The graph is directed, which means it functions more like a flow chart instead of a plot of relations. The graph is induced from unstructured text found online, so it is a general and scalable approach.

Learn Before

Related

Learn After