  We introduce an algorithm to locate contours of functions that are expensive
to evaluate. The problem of locating contours arises in many applications,
including classification, constrained optimization, and performance analysis of
mechanical and dynamical systems (reliability, probability of failure,
stability, etc.). Our algorithm locates contours using information from
multiple sources, which are available in the form of relatively inexpensive,
biased, and possibly noisy approximations to the original function. Considering
multiple information sources can lead to significant cost savings. We also
introduce the concept of contour entropy, a formal measure of uncertainty about
the location of the zero contour of a function approximated by a statistical
surrogate model. Our algorithm locates contours efficiently by maximizing the
reduction of contour entropy per unit cost.
