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Mor Vered, Gal A.
Kaminka, and Sivan Biham. Online Goal Recognition through Mirroring: Humans and Agents. In Proceedings of
the Annual Conference on Advances in Cognitive Systems, 2016. A slightly modified version appears in Proceedings of the
IJCAI 2016 workshop on Human-Agent Interaction Design and Models (HAIDM)
Goal recognition is the problem of inferring the (unobserved) goal of an agent, basedon a sequence of its observed actions. Inspired by mirroring processes in human brains,we advocate goal mirroring, an online recognition approach thatuses a black-box planner to generate recognition hypotheses. This approach avoidsthe prevalent assumption in current approaches, which rely on a dedicated plan library,representing all known plans to achieve known goals.Such methods are inherently limited to the knowledge represented in the library.In this paper, we (i) describe a novel online goal mirroring algorithm for continuous spaces;(ii) evaluate a novel heuristic for choosing between competing recognition hypotheses; (iii) contrastmachine and human recognition in two challenging domains, revealing insights as to human capabilities; and (iv)compare mirroring to library-based methods.
@InProceedings{acs16,
author = {Mor Vered and Gal A. Kaminka and Sivan Biham},
title = {Online Goal Recognition through Mirroring: Humans and Agents},
booktitle = {Proceedings of the Annual Conference on Advances in Cognitive Systems},
OPTcrossref = {crossref},
OPTkey = {key},
OPTpages = {pages},
year = {2016},
OPTeditor = {editor},
OPTvolume = {volume},
OPTnumber = {number},
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OPTaddress = {address},
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note = {A slightly modified version appears in Proceedings of the {IJCAI} 2016 workshop on Human-Agent Interaction Design and Models (HAIDM)},
OPTannote = {annote},
abstract = {
Goal recognition is the problem of inferring the (unobserved) goal of an agent, based
on a sequence of its observed actions. Inspired by mirroring processes in human brains,
we advocate \emph{goal mirroring}, an online recognition approach that
uses a black-box planner to generate recognition hypotheses. This approach avoids
the prevalent assumption in current approaches, which rely on a dedicated \emph{plan library},
representing all known plans to achieve known goals.
Such methods are inherently limited to the knowledge represented in the library.
In this paper, we (i) describe a novel online goal mirroring algorithm for continuous spaces;
(ii) evaluate a novel heuristic for choosing between competing recognition hypotheses; (iii) contrast
machine and human recognition in two challenging domains, revealing insights as to human capabilities; and (iv)
compare mirroring to library-based methods.
},
wwwnote = {},
OPTkeywords = {},
}
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