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A legal planning stack that employs Defeasible Deontic Logic (DDL) to constrain a motion planner and leverages learned world models to plan and to provide legal context, enabling ex ante governance that intervenes before an illegal action is executed.
This letter contributes a planner-agnostic constraint-tightening procedure that equips existing planners with a probabilistic task-completion guarantee on the true system, and validate the theoretical guarantees empirically and demonstrate substantially improved task completion relative to nominal-model planning.
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