Proceedings of the Annual Meeting of the Cognitive Science Society

Most Natural Language Generation systems developed to date assume that a user will learn only what is explicitly stated in the discourse. This assumption leads to the generation of discourse that slates explicitly all the information to be conveyed, and that does not address further inferences from the discourse. The content planning mechanism described in this paper addresses these problems by taking into consideration the inferences the user is likely to make from the presented information. These inferences are modeled by means of inference rules, which are applied in a prescriptive manner to generate discourse that conveys the intended information, and in a predictive mode to draw further conclusions from the presented information. In addition, our mechanism minimizes the generated discourse by presenting only information the user does not know or about which s/he has misconceptions. The domain of our implementation is the explanation of concepts in high school algebra.