Gal A. Kaminka: Publications

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Plan Recognition in Continuous Domains

Gal A. Kaminka, Mor Vered, and Noa Agmon. Plan Recognition in Continuous Domains. In Proceedings of the AAAI Conference on Artificial Intelligence , 2018.

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Abstract

Plan recognition is the task of inferring the plan of an agent, basedon an incomplete sequence of its observed actions. Previous formulations of planrecognition commit early to discretizations of the environment and the observedagent's actions. This leads to reduced recognition accuracy. To address this, wefirst provide a formalization of recognition problems which admits continuousenvironments, as well as discrete domains. We then show that throughmirroring---generalizing plan-recognition by planning---wecan apply continuous-world motion planners in plan recognition.We provide formal arguments for the usefulness of mirroring, and empiricallyevaluate mirroring in more than a thousand recognition problemsin three continuous domains and six classical planning domains.

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BibTeX

@inproceedings{aaai18,
	author = {Gal A. Kaminka and Mor Vered and Noa Agmon},
title = {Plan Recognition in Continuous Domains},
booktitle = AAAI,
OPTcrossref = {crossref},
OPTkey = {key},
OPTpages = {pages},
year = {2018},
OPTeditor = {editor},
OPTvolume = {volume},
OPTnumber = {number},
OPTseries = {series},
OPTaddress = {address},
OPTmonth = {month},
OPTorganization = {organization},
OPTpublisher = {publisher},
OPTnote = {An earlier version was published in the IJCAI 2017 workshop on Goal Reasoning.},
OPTannote = {annote},
wwwnote = {},
OPTkeywords = {},
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
\textit{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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