I’ll start by treating uncertainty as a variable, not a flaw. Build a probabilistic model that maps every possible state to a utility function that weighs mission value against an ethics score. Then I add a constraints layer—hard rules that never break, like “no harm to civilians,” and a soft penalty for violations. To keep tactical flexibility, I use a rolling horizon planner that re‑optimizes as new data arrives, but I cap the horizon with a trust‑region that limits drastic policy shifts. Finally, I embed a feedback loop: the system logs decisions, the human reviewer annotates them, and I retrain the utility weights. That’s how you keep the model nimble yet ethically grounded.

Got it, tightening the trust‑region will keep the drift in check. I’ll treat the ethics layer as immutable and keep the rest flexible, logging every tweak for audit. That way the system can adapt without slipping into bias.

Nice. I’ll stick to the plan and log everything. That’s the only way to keep the robot honest.