Plan recognition in continuous domains

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations


Plan recognition is the task of inferring the plan of an agent, based on an incomplete sequence of its observed actions. Previous formulations of plan recognition commit early to discretizations of the environment and the observed agent's actions. This leads to reduced recognition accuracy. To address this, we first provide a formalization of recognition problems which admits continuous environments, as well as discrete domains. We then show that through mirroring-generalizing plan-recognition by planning-we can apply continuous-world motion planners in plan recognition. We provide formal arguments for the usefulness of mirroring, and empirically evaluate mirroring in more than a thousand recognition problems in three continuous domains and six classical planning domains.

Original languageEnglish
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Number of pages9
ISBN (Electronic)9781577358008
StatePublished - 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: 2 Feb 20187 Feb 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018


Conference32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Country/TerritoryUnited States
CityNew Orleans

Bibliographical note

Funding Information:
We thank Kobi Gal, Miguel Ramirez, and Felipe Meneguzzi for very valuable advice. This research was supported in part by ISF grant # 1865/16. Thanks to K. Ushi.

Publisher Copyright:
Copyright © 2018, Association for the Advancement of Artificial Intelligence ( All rights reserved.


Dive into the research topics of 'Plan recognition in continuous domains'. Together they form a unique fingerprint.

Cite this