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