Landmark-Based Heuristics for Goal Recognition

Ramon Fraga Pereira, Nir Oren, Felipe Meneguzzi

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

65 Citations (Scopus)
19 Downloads (Pure)


Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks — facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.
Original languageEnglish
Title of host publicationThirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
PublisherAAAI Press
Number of pages7
Publication statusPublished - Aug 2017
EventThirty-First AAAI Conference on Artificial Intelligence (AAAI-17) - San Francisco, United States
Duration: 4 Feb 20179 Feb 2017

Publication series

NameAAAI Conference on Artificial Intelligence (AAAI)
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


ConferenceThirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
Country/TerritoryUnited States
CitySan Francisco

Bibliographical note

6 volumes


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