Landmark-based approaches for goal recognition as planning

Ramon Fraga Pereira*, Nir Oren, Felipe Meneguzzi

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Recognizing goals and plans from complete or partial observations can be efficiently achieved through 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 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 result in substantially faster recognition time over existing techniques.
Original languageEnglish
Article number103217
JournalArtificial Intelligence
Volume279
Early online date4 Dec 2019
DOIs
Publication statusPublished - Feb 2020

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Keywords

  • goal recognition
  • AI planning
  • landmarks
  • Goal recognition
  • STRIPS
  • Landmarks

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