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In this paper we design coordination policies for unmanned vehicles that select and perform tasks in uncertain environments where vehicles may fail. We develop algorithms that accept different levels of human guidance, from simple allocation of priorities through the use of task values to more complex task partitioning and load balancing techniques. The goal is to maximize expected value completed under human guidance. We develop alternative algorithms based on approximate dynamic programming versions appropriate for each level of guidance, and compare the resulting performance using simulation results.