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Dorit Avrahami-Zilberbrand, Gal
A. Kaminka, and Hila Zarosim. Fast and complete plan recognition: Allowing for duration, interleaved execution, and
lossy observations. In Proceedings of the IJCAI Workshop on Modeling Others from Observations (MOO-05), 2005.
It is important for agents to model other agents’ unobserved plans and goals, based on their observable actions. This process of modeling others based on observations is known as plan-recognition. Plan recognition has been studied for many years. It often takes the form of matching observations of an agent’s actions to a plan-library, a model of possible plans selected by the agent. However, there are several open key challenges in modern plan recognition: (i) handling lossy observations (where an observation or a component of an observation is intermittently lost); (ii) dealing with plan execution duration constraints; and (iii) interleaved plans (where an agent interrupts a plan for another, only to return to the first later). In this paper, we present efficient algorithms that address these challenges, in the context of symbolic plan recognition. The algorithms allow (i) efficient matching of (possibly lossy) observations to a plan library; (ii) efficient computation of all recognition hypotheses consistent with the observations, subject to interleaving and duration constraints.
@InProceedings{moo05dorit,
author = {Dorit Avrahami-Zilberbrand and Gal A. Kaminka and Hila Zarosim},
title = {Fast and complete plan recognition: Allowing for duration, interleaved execution, and lossy observations},
OPTcrossref = {},
OPTkey = {},
booktitle = MOO-05,
OPTpages = {},
year = {2005},
abstract = {
It is important for agents to model other agents’ unobserved plans
and goals, based on their observable actions. This process of
modeling others based on observations is known as
plan-recognition. Plan recognition has been studied for many
years. It often takes the form of matching observations of an
agent’s actions to a plan-library, a model of possible plans
selected by the agent. However, there are several open key
challenges in modern plan recognition: (i) handling lossy
observations (where an observation or a component of an observation
is intermittently lost); (ii) dealing with plan execution
duration constraints; and (iii) interleaved plans (where an agent
interrupts a plan for another, only to return to the ï¬rst
later). In this paper, we present efï¬cient algorithms that address
these challenges, in the context of symbolic plan recognition. The
algorithms allow (i) efï¬cient matching of (possibly lossy)
observations to a plan library; (ii) efï¬cient computation of all
recognition hypotheses consistent with the observations, subject
to interleaving and duration constraints.
},
wwwnote = {},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTnote = {},
OPTannote = {}
}
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