  The task of recognizing goals and plans from missing and full observations
can be done efficiently by using automated planning techniques. In many
applications, it is important to recognize goals and plans not only accurately,
but also quickly. To address this challenge, we develop novel goal recognition
approaches based on planning techniques that rely on planning landmarks. In
automated planning, landmarks are properties (or actions) that cannot be
avoided to achieve a goal. We show the applicability of a number of planning
techniques with an emphasis on landmarks for goal and plan recognition tasks in
two settings: (1) we use the concept of landmarks to develop goal recognition
heuristics; and (2) we develop a landmark-based filtering method to refine
existing planning-based goal and plan recognition approaches. These recognition
approaches are empirically evaluated in experiments over several classical
planning domains. We show that our goal recognition approaches yield not only
accuracy comparable to (and often higher than) other state-of-the-art
techniques, but also substantially faster recognition time over such
techniques.
