  Resilient submodular maximization refers to the combinatorial problems
studied by Nemhauser and Fisher and asks how to maximize an objective given a
number of adversarial removals. For example, one application of this problem is
multi-robot sensor planning with adversarial attacks. However, more general
applications of submodular maximization are also relevant. Tzoumas et al.
obtain near-optimal solutions to this problem by taking advantage of a property
called curvature to produce a mechanism which makes certain bait elements
interchangeable with other elements of the solution that are produced via
typical greedy means. This document demonstrates that -- at least in theory --
applying the method for selection of bait elements to the entire solution can
improve that guarantee on solution quality.
