TY - GEN

T1 - The knowledge gradient algorithm using locally parametric approximations

AU - Cheng, Bolong

AU - Jamshidi, Arta A.

AU - Powell, Warren Buckler

PY - 2013

Y1 - 2013

N2 - We are interested in maximizing a general (but continuous) function where observations are noisy and may be expensive. We derive a knowledge gradient policy, which chooses measurements which maximize the expected value of information, while using a locally parametric belief model which uses linear approximations around regions of the function, known as clouds. The method, called DC-RBF (Dirichlet Clouds with Radial Basis Functions) is well suited to recursive estimation, and uses a compact representation of the function which avoids storing the entire history. Our technique allows for correlated beliefs within adjacent subsets of the alternatives and does not pose any a priori assumption on the global shape of the underlying function. Experimental work suggests that the method adapts to a range of arbitrary, continuous functions, and appears to reliably find the optimal solution.

AB - We are interested in maximizing a general (but continuous) function where observations are noisy and may be expensive. We derive a knowledge gradient policy, which chooses measurements which maximize the expected value of information, while using a locally parametric belief model which uses linear approximations around regions of the function, known as clouds. The method, called DC-RBF (Dirichlet Clouds with Radial Basis Functions) is well suited to recursive estimation, and uses a compact representation of the function which avoids storing the entire history. Our technique allows for correlated beliefs within adjacent subsets of the alternatives and does not pose any a priori assumption on the global shape of the underlying function. Experimental work suggests that the method adapts to a range of arbitrary, continuous functions, and appears to reliably find the optimal solution.

UR - http://www.scopus.com/inward/record.url?scp=84894216980&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84894216980&partnerID=8YFLogxK

U2 - 10.1109/WSC.2013.6721477

DO - 10.1109/WSC.2013.6721477

M3 - Conference contribution

AN - SCOPUS:84894216980

SN - 9781479939503

T3 - Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013

SP - 856

EP - 867

BT - Proceedings of the 2013 Winter Simulation Conference - Simulation

T2 - 2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013

Y2 - 8 December 2013 through 11 December 2013

ER -