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The task of recognizing goals and plans from missing and full observationscan be done efficiently by using automated planning techniques. In manyapplications, it is important to recognize goals and plans not only accurately,but also quickly. To address this challenge, we develop novel goal recognitionapproaches based on planning techniques that rely on planning landmarks. Inautomated planning, landmarks are properties (or actions) that cannot beavoided to achieve a goal. We show the applicability of a number of planningtechniques with an emphasis on landmarks for goal and plan recognition tasks intwo settings: (1) we use the concept of landmarks to develop goal recognitionheuristics; and (2) we develop a landmark-based filtering method to refineexisting planning-based goal and plan recognition approaches. These recognitionapproaches are empirically evaluated in experiments over several classicalplanning domains. We show that our goal recognition approaches yield not onlyaccuracy comparable to (and often higher than) other state-of-the-arttechniques, but also substantially faster recognition time over suchtechniques.

Landmark-Based Approaches for Goal Recognition as Planning