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Dorit Avrahami-Zilberbrand and
Gal A. Kaminka. Utility-Based Plan Recognition: An Extended Abstract (Short Paper). In Proceedings of the
Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-07), 2007.
Plan recognition is the process of inferring other agents' plansand goals based on their observable actions. Essentially allprevious work in plan recognition has focused on the recognitionprocess itself, with no regard to the use of the information inthe recognizing agent. As a result, low-likelihood recognitionhypotheses that may imply significant meaning to the observer, areignored in existing work. In this paper, we present novelefficient algorithms that allows the observer to incorporate herown biases and preferences---in the form of a utilityfunction---into the plan recognition process. This allows choosingrecognition hypotheses based on their expected utility to theobserver. We call this Utility-based Plan Recognition (UPR). We briefly discuss a hybrid symbolicdecision-theoretic plan recognizer, and demonstrate the efficacy of thisapproach in an example.
@InProceedings{aamas07dorit,
author = {Dorit Avrahami-Zilberbrand and Gal A. Kaminka},
title = {Utility-Based Plan Recognition: An Extended Abstract (Short Paper)},
booktitle = AAMAS-07,
OPTcrossref = {},
OPTkey = {},
OPTpages = {},
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). We briefly discuss a hybrid symbolic
decision-theoretic plan recognizer, and demonstrate the efficacy of this
approach in an example. },
wwwnote = {},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTnote = {},
OPTannote = {}
}
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