Quality vehicle health management systems are critical to the successful operation of modern sounding rockets, and other unmanned vehicles. Unfortunately, the software of these systems tends to be complex and rigid and thus expensive and failure-prone, especially given the several real-time constraints of rocketry. We propose to develop the ``Magic Bullet'' Adaptive Intelligent Vehicle Health Management (AIVHM) System, a novel adaptive control system for sounding rockets based on the technologies of treatment learning and Bayes classification. This system will be able to derive an appropriate control strategy for a vehicle in the event of partial system failure. Our relationship with the Portland State Aerospace Society (PSAS) provides us with a unique opportunity to evaluate and deploy these methods at extremely low cost and with extremely low risk, for simulation and even actual flight testing. The PSAS LV2 rocket has a navigation and control system architecture ideally suited to experimentation with the proposed system. As senior technical advisory to PSAS, our organization is well-positioned to prototype and deploy the Magic Bullet AIVHMs technology with PSAS. We expect this deployment to result in the information needed to scale the technology to larger, more complex, more demanding avionics applications.