TY - GEN
T1 - Observation subset selection as local compilation of performance profiles
AU - Radovilsky, Yan
AU - Eyal Shimony, Solomon
PY - 2008/12/1
Y1 - 2008/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77956027270&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77956027270
SN - 0974903949
SN - 9780974903941
T3 - Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
SP - 460
EP - 467
BT - Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
T2 - 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
Y2 - 9 July 2008 through 12 July 2008
ER -