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Mor Vered and Gal
A. Kaminka. Heuristic Online Goal Recognition in Continuous Domains. In Proceedings of the International Joint
Conference on Artificial Intelligence, pp. 4447–4454, 2017. An improved version (with minor corrections) is available
as arxiv:1709.09839
Goal recognition is the problem of inferring the goal of an agent, based on its observed actions. An inspiring approach---plan recognition by planning (PRP)---uses off-the-shelf planners to dynamically generate plans for given goals, eliminating the need for the traditional plan library. However, existing PRP formulation is inherently inefficient in online recognition, and cannot be used with motion planners for continuous spaces. In this paper, we utilize a different PRP formulation which allows for online goal recognition, and for application in continuous spaces. We present an online recognition algorithm, where two heuristic decision points may be used to improve run-time significantly over existing work. We specify heuristics for continuous domains, prove guarantees on their use, and empirically evaluate the algorithm over hundreds of experiments in both a 3D navigational environment and a cooperative robotic team task.
@InProceedings{ijcai17,
author = {Mor Vered and Gal A. Kaminka},
title = {Heuristic Online Goal Recognition in Continuous Domains},
booktitle = IJCAI,
OPTcrossref = {crossref},
OPTkey = {key},
pages = {4447--4454},
year = {2017},
OPTeditor = {editor},
OPTvolume = {volume},
OPTnumber = {number},
OPTseries = {series},
OPTaddress = {address},
OPTmonth = {month},
OPTorganization = {organization},
OPTpublisher = {publisher},
note = {An improved version (with minor corrections) is available as arxiv:1709.09839},
OPTannote = {annote},
wwwnote = {},
OPTkeywords = {},
abstract = {
Goal recognition is the problem of inferring the goal of an agent, based on its observed actions.
An inspiring approach---plan recognition by planning (PRP)---uses off-the-shelf planners to
dynamically generate plans for given goals, eliminating the need for the traditional plan library.
However, existing PRP formulation is inherently inefficient in online recognition,
and cannot be used with motion planners for continuous spaces.
In this paper, we utilize a different PRP formulation which
allows for online goal recognition, and for application in continuous spaces.
We present an online recognition algorithm, where two heuristic decision points
may be used to improve run-time significantly over existing work.
We specify heuristics for continuous domains, prove guarantees on their use, and empirically evaluate the algorithm over
hundreds of experiments in both a 3D navigational environment and a cooperative robotic team task.
},
}
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