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Recent approaches to goal recognition have leveraged planning landmarks toachieve high-accuracy with low runtime cost. These approaches, however, lack aprobabilistic interpretation. Furthermore, while most probabilistic models togoal recognition assume that the recognizer has access to a prior probabilityrepresenting, for example, an agent's preferences, virtually no goalrecognition approach actually uses the prior in practice, simply assuming auniform prior. In this paper, we provide a model to both extend landmark-basedgoal recognition with a probabilistic interpretation and allow the estimationof such prior probability and its usage to compute posterior probabilitiesafter repeated interactions of observed agents. We empirically show that ourmodel can not only recognize goals effectively but also successfully infer thecorrect prior probability distribution representing an agent's preferences.

Inferring Agents Preferences as Priors for Probabilistic Goal Recognition