In this paper we present a new approach to plan understanding that explains observed actions in terms of domain knowledge. The process operates over hierarchical methods and utilizes an incremental form of data-driven abductive inference. We report experiments on problems from the Monroe corpus that demonstrate a basic ability to construct plausible explanations, graceful degradation of performance with reduction of the fraction of actions observed, and results with incremental processing that are comparable to batch interpretation. We also discuss research on related tasks such as plan recognition and abductive construction of explanations.
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