We describe an approach for learning a rich plan representation from a parallel corpus of commonsense narratives. Each narrative is an ordered natural language description of the steps required to accomplish common domestic tasks, including “get the mail ” and “make a bed”, and there are tens to hundreds of differently written narratives for each task. With the goal of learning a single rich plan structure, we 1) convert each narrative from English statements into a sequence of logical predicates, 2) find a global alignment for the sequences, and 3) use the sequences to construct a single underlying plan representation that can be used in language understanding problems. Doing this requires being able to distinguish different ways to accomplish the same goal from missing information, and recognize and compactly represent recurring plan sub-sequences. We describe a simple algorithm that recursively finds graph cycles by applying rules to merge nodes to learn a sequential, parameterized composition (part-of) and abstraction (is-a) plan hierarchy. We hope that these plan representations will help us learn procedural knowledge from increasingly more sophisticated text, where the sub-goals for various actions are not stated. Author Keywords Common sense, knowledge acquisition, machine reading, natural language understanding, story knowledge, plan construction, textual entailment ACM Classification Keywords H.5.2 Information Interfaces and Presentation: User Interfaces—Natural Language

Abstract: Drawing on the projected increase in computing power, solid-state storage and network communication capacity to be available on personal mobile devices, we propose to build and maintain without prior knowledge a fully distributed decentralized large-scale model of the physical world around us using probabilistic methods. We envisage that, by using the multimodal sensing capabilities of modern personal devices, such a probabilistic world model can be constructed as a collaborative effort of a community of participants, where the model data is redundantly stored on individual devices and updated and refined through short-range wireless peer-to-peer communication. Every device holds model data describing its current surroundings, and obtains model data from others when moving into unknown territory. The model represents common spatio-temporal patterns as observed by multiple participants, so that rogue participants can not easily insert false data and only patterns of general applicability dominate. This paper aims to describe – at a conceptual level – an approach for building such a distributed world model. As one possible world modeling approach, it discusses compositional hierarchies, to fuse the data from multiple sensors available on mobile devices in a bottom-up way. Furthermore, it focuses on the intertwining between building a decentralized cooperative world model and the opportunistic communication between participants.

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