Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. This task is difficult because written descriptions often do not specify exactly how referenced events fit together. This article (1) characterizes the causal reconstruction problem, (2) presents a representation called transition space, which portrays events in terms of "transitions," or collections of changes expressible in everydaylanguage, and (3) describes a program called PATHFINDER, which uses the transition space representation to perform causal reconstruction on simplified English descriptions of physical activity.PATHFINDER works byidentifying partial matches between the representations of events and using these matches to form causal chains, fill causal gaps, and merge overlapping accounts of activity. By applying transformations to events prior to matching, PATHFINDER is also able to handle a range of discontinuities arising from a writer's use of analogy or abstraction.
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Massachusetts Institute of Technology, AI Lab, memo