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Temporal planning is an active research area of Artificial Intelligence because of its many applications ranging from roboticsto logistics and beyond. Traditionally, authors focused on theautomatic synthesis of plans given a formal representation of thedomain and of the problem. However, the effectiveness of suchtechniques is limited by the complexity of the modeling phase: it ishard to produce a correct model for the planning problem at hand. In this paper, we present a technique to simplify the creation ofcorrect models by leveraging formal-verification tools for automaticvalidation. We start by using the ANML language, a very expressivelanguage for temporal planning problems that has been recentlypresented. We chose ANML because of its usability andreadability. Then, we present a sound-and-complete, formal encodingof the language into Linear Temporal Logic over predicates withinfinite-state variables. Thanks to this reduction, we enable theformal verification of several relevant properties over the planningproblem, providing useful feedback to the modeler.