Using Plan Reasoning in the Generation of Plan Descriptions

R. Michael Young

Previous work on the generation of natural language descriptions of complex activities has indicated that the unwieldy amount of text needed to describe complete plans makes for ineffective and unnatural descriptions. We argue here that concise and effective text descriptions of plans can be generated by exploiting a model of the hearer’s plan reasoning capabilities. We define a computational model of the hearer’s interpretation process that views the interpretation of plan descriptions as refinement search through a space of partial plans. This model takes into account the hearer’s plan preferences and the resource limitations on her reasoning capabilities to determine the completed plans she will construct from a given partial description.


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