  Commonsense procedural knowledge is important for AI agents and robots that
operate in a human environment. While previous attempts at constructing
procedural knowledge are mostly rule- and template-based, recent advances in
deep learning provide the possibility of acquiring such knowledge directly from
natural language sources. As a first step in this direction, we propose a model
to learn embeddings for tasks, as well as the individual steps that need to be
taken to solve them, based on WikiHow articles. We learn these embeddings such
that they are predictive of both step relevance and step ordering. We also
experiment with the use of integer programming for inferring consistent global
step orderings from noisy pairwise predictions.
