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Dorit Avrahami-Zilberbrand and
Gal A. Kaminka. Incorporating Observer Biases in Keyhole Plan Recognition (Efficiently!). In Proceedings
of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07) , pp. 944–949, 2007.
Plan recognition is the process of inferring other agents' plansand goals based on their observable actions. Essentially all previous work in planrecognition has focused on the recognition process itself, with noregard to the use of the information in the recognizing agent.As a result, low-likelihood recognition hypotheses that may imply significantmeaning to the observer, are ignored in existing work. In this paper, we presentnovel efficient algorithms that allows the observer to incorporate herown biases and preferences---in the form of a utility function---into the plan recognition process.This allows choosing recognition hypotheses based on their expected utility to the observer. We callthis Utility-based Plan Recognition (UPR).While reasoning about such expected utilities is intractable in the general case, we presenta hybrid symbolic/decision-theoretic plan recognizer, whose complexity is $O(NDT)$, where $N$ isthe plan library size, $D$ is the depth of the library and $T$ is the number of observations.We demonstrate the efficacy of this approach with experimental results in several challengingrecognition tasks.
@InProceedings{aaai07upr,
author = {Dorit Avrahami-Zilberbrand and Gal A. Kaminka},
title = {Incorporating Observer Biases in Keyhole Plan Recognition (Efficiently!)},
OPTcrossref = {},
OPTkey = {},
booktitle = AAAI-07,
pages = {944--949},
year = {2007},
abstract = { Plan recognition is the process of inferring other agents' plans
and goals based on their observable actions. Essentially all previous work in plan
recognition has focused on the recognition process itself, with no
regard to the use of the information in the recognizing agent.
As a result, low-likelihood recognition hypotheses that may imply significant
meaning to the observer, are ignored in existing work. In this paper, we present
novel efficient algorithms that allows the observer to incorporate her
own biases and preferences---in the form of a utility function---into the plan recognition process.
This allows choosing recognition hypotheses based on their expected utility to the observer. We call
this Utility-based Plan Recognition (UPR).
While reasoning about such expected utilities is intractable in the general case, we present
a hybrid symbolic/decision-theoretic plan recognizer, whose complexity is $O(NDT)$, where $N$ is
the plan library size, $D$ is the depth of the library and $T$ is the number of observations.
We demonstrate the efficacy of this approach with experimental results in several challenging
recognition tasks. },
wwwnote = {},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTannote = {}
}
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