Plan recognition in continuous domains

Gal A. Kaminka, Mor Vered, Noa Agmon

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

Abstract

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 publicationProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18 )
Subtitle of host publicationNew Orleans, Louisiana USA — February 2–7, 2018
EditorsSheila McIlraith, Kilian Weinberger
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages6202-6210
Number of pages9
ISBN (Electronic)9781577358008
Publication statusPublished - 2018
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2018 - New Orleans, United States of America
Duration: 2 Feb 20187 Feb 2018
Conference number: 32nd
https://aaai.org/Conferences/AAAI-18/

Conference

ConferenceAAAI Conference on Artificial Intelligence 2018
Abbreviated titleAAAI 2018
CountryUnited States of America
CityNew Orleans
Period2/02/187/02/18
Internet address

Keywords

  • Plan Recognition
  • Continuous Domains
  • Mirroring

Cite this

Kaminka, G. A., Vered, M., & Agmon, N. (2018). Plan recognition in continuous domains. In S. McIlraith, & K. Weinberger (Eds.), Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18 ): New Orleans, Louisiana USA — February 2–7, 2018 (pp. 6202-6210). Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI).
Kaminka, Gal A. ; Vered, Mor ; Agmon, Noa. / Plan recognition in continuous domains. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18 ): New Orleans, Louisiana USA — February 2–7, 2018. editor / Sheila McIlraith ; Kilian Weinberger. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2018. pp. 6202-6210
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title = "Plan recognition in continuous domains",
abstract = "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.",
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Kaminka, GA, Vered, M & Agmon, N 2018, Plan recognition in continuous domains. in S McIlraith & K Weinberger (eds), Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18 ): New Orleans, Louisiana USA — February 2–7, 2018. Association for the Advancement of Artificial Intelligence (AAAI), Palo Alto CA USA, pp. 6202-6210, AAAI Conference on Artificial Intelligence 2018, New Orleans, United States of America, 2/02/18.

Plan recognition in continuous domains. / Kaminka, Gal A.; Vered, Mor; Agmon, Noa.

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18 ): New Orleans, Louisiana USA — February 2–7, 2018. ed. / Sheila McIlraith; Kilian Weinberger. Palo Alto CA USA : Association for the Advancement of Artificial Intelligence (AAAI), 2018. p. 6202-6210.

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

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AB - 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.

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Kaminka GA, Vered M, Agmon N. Plan recognition in continuous domains. In McIlraith S, Weinberger K, editors, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18 ): New Orleans, Louisiana USA — February 2–7, 2018. Palo Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI). 2018. p. 6202-6210