Advances in a fuzzy decision theory that allow automatic cooperation between unmanned aerial vehicles (UAVs) are discussed. The algorithms determine points the UAVs are to sample, flight paths, and the optimal UAVs for the task and related changes during the mission. Human intervention is not required after the mission begins. The algorithms take into account what is known before and during the mission about UAV reliability, fuel, and kinematics as well as the measurement space's meteorological states, terrain, air traffic, threats and related uncertainties. The fuzzy decision tree for path assignment is a significant advance over an older fuzzy decision rule that was previously introduced. Simulations show the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team's likelihood of successfully measuring the atmospheric index of refraction over a large volume. A genetic program (GP) based data mining procedure is discussed for automatically evolving fuzzy decision trees. The GP is used to automatically create the fuzzy decision tree for real-time UAV path assignments. The GP based procedure offers several significant advances over previously introduced GP based data mining procedures. These advances help produce mathematically concise fuzzy decision trees that are consistent with human intuition.
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