How do people write letters? Examine the contents of any letter-writing handbook. People gain proficiency in this form of discourse through adaptation of examples (or at least there is a wide consensus that examples are an excellent way to teach good writing skills). LetterGen constructs letters in a similar manner: The programmer initially separates example letters into snippets, and provides a plan derivation for each snippet. During an interview with the user, LetterGen infers which snippets are relevant to the user's stated goals and beliefs by instantiating and adapting the stored derivations. Snippets are then ordered into a new, complete letter. Additionally, representing letters as a set of plan derivations has the consequence that translated versions of a letter do not require special treatment; the target language is treated as just one of many goals.

Advice-giving is improved by understanding and responding to user feedback. Previous models of this task treat user responses as misunderstandings or misconceptions and focus on generating altemate or corrective explanations. By and large, however, these models do not consider the possibility that the system's advice is inappropriate and may need revision during the course of an on-going dialog. This paper presents a model of the process of revising plan-oriented advice in response to user feedback. Our focus is on mechanisms for evaluating planning alternatives, for detemiining when to revise advice, and for dynamically generating explanations of these revisions.