**Description**
This paper first describes the utilization of a Virtual and Constructive (VC) Simulation to copy the level of performance of a jet fighter pilot. The experiments used one to three jet fighters (the respective pilot must conduct one). The remaining ones are by agents of the constructive environment. The landscape will have the jet fighters attempting to destroy the control towers behind one to two SA-8 TELAR surface-to-air missiles (SAMs), which can fire at the jet fighters. Each experiment captured information on the features of the agents (missiles, strategy) and photographs of the positions of the SA-8 TELARs as captured by simulated air surveillance and reconnaissance system. Second, the experiments' results and context are utilized for training a complex Convolutional Neural Network (CNN), which can handle the recognition of photographs. Third, this CNN is approximated by a backpropagation neural network. An automated fighter pilot's capabilities are constrained by having this neural network as part of its decision-making (to pursue or abandon the theater of operations). Finally, a new agent-based environment is built to test this automated pilot. This new agent-based environment uses the paradigm of state of charts to represent radars, jet fighters, SA-8 TELARs, missiles, etc.

**Author(s) - ranked in descending order of contribution / as it should appear when published**
Larry Lowe
Luis Rabelo
Marwen Elkamel
Mitchell Hunsucker
Katalina Arias-Marin
Nathalia Davila
Omar Allaz
Gene Lee



Reproducing Pilot Performance Using VC Simulation, Deep Learning, and Agents

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