  Recent approaches to goal recognition have progressively relaxed the
assumptions about the amount and correctness of domain knowledge and available
observations, yielding accurate and efficient algorithms. These approaches,
however, assume completeness and correctness of the domain theory against which
their algorithms match observations: this is too strong for most real-world
domains. In this paper, we develop goal recognition techniques that are capable
of recognizing goals using \textit{incomplete} (and possibly incorrect) domain
theories. We show the efficiency and accuracy of our approaches empirically
against a large dataset of goal and plan recognition problems with incomplete
domains.
