Gal A. Kaminka's Publications

Sorted by DateClassified by Publication TypeClassified by TopicGrouped by Student (current)Grouped by Former Students

Plan-Recognition as Planning in Continuous and Discrete Domains

Gal A. Kaminka, Mor Vered, and Noa Agmon. Plan-Recognition as Planning in Continuous and Discrete Domains. In IJCAI Workshop on Goal Reasoning, 2017. A much improved version was published in the AAAI 2018 conference.

Download

(unavailable)

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.

Additional Information

BibTeX

@inproceedings{goalreason17ws,
	author = {Gal A. Kaminka and Mor Vered and Noa Agmon},
	title = {Plan-Recognition as Planning in Continuous and Discrete Domains},
	booktitle = {{IJCAI} Workshop on Goal Reasoning},
	OPTcrossref = {crossref},
	OPTkey = {key},
	OPTpages = {pages},
	year = {2017},
	OPTeditor = {editor},
	OPTvolume = {volume},
	OPTnumber = {number},
	OPTseries = {series},
	OPTaddress = {address},
	OPTmonth = {month},
	OPTorganization = {organization},
	OPTpublisher = {publisher},
	note = {A much improved version was published in the AAAI 2018 conference.},
	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.
	},	
}

Generated by bib2html.pl (written by Patrick Riley ) on Sat Feb 24, 2018 00:31:02