Observation subset selection as local compilation of performance profiles

Yan Radovilsky, Solomon Eyal Shimony

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Deciding what to sense is a crucial task, made harder by dependencies and by a nonadditive utility function. We develop approximation algorithms for selecting an optimal set of measurements, under a dependency structure modeled by a tree-shaped Bayesian network (BN). Our approach is a generalization of composing anytime algorithm represented by conditional performance profiles. This is done by relaxing the input monotonicity assumption, and extending the local compilation technique to more general classes of performance profiles (PPs). We apply the extended scheme to selecting a subset of measurements for choosing a maximum expectation variable in a binary valued BN, and for minimizing the worst variance in a Gaussian BN.

Original languageEnglish
Title of host publicationProceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
Pages460-467
Number of pages8
StatePublished - 1 Dec 2008
Event24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 - Helsinki, Finland
Duration: 9 Jul 200812 Jul 2008

Publication series

NameProceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Conference

Conference24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
Country/TerritoryFinland
CityHelsinki
Period9/07/0812/07/08

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