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

Learning Household Task Knowledge from WikiHow Descriptions