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In automated planning, the need for explanations arises when there is amismatch between a proposed plan and the user's expectation. We frameExplainable AI Planning in the context of the plan negotiation problem, inwhich a succession of hypothetical planning problems are generated and solved.The object of the negotiation is for the user to understand and ultimatelyarrive at a satisfactory plan. We present the results of a user study thatdemonstrates that when users ask questions about plans, those questions arecontrastive, i.e. "why A rather than B?". We use the data from this study toconstruct a taxonomy of user questions that often arise during plannegotiation. We formally define our approach to plan negotiation through modelrestriction as an iterative process. This approach generates hypotheticalproblems and contrastive plans by restricting the model through constraintsimplied by user questions. We formally define model-based compilations inPDDL2.1 of each constraint derived from a user question in the taxonomy, andempirically evaluate the compilations in terms of computational complexity. Thecompilations were implemented as part of an explanation framework that employsiterative model restriction. We demonstrate its benefits in a second userstudy.

Contrastive Explanations of Plans Through Model Restrictions