  Advanced Air Mobility (AAM) is a growing field that demands a deep
understanding of legal, spatial and temporal concepts in navigation. Hence, any
implementation of AAM is forced to deal with the inherent uncertainties of
human-inhabited spaces. Enabling growth and innovation requires the creation of
a system for safe and robust mission design, i.e., the way we formalize
intentions and decide their execution as trajectories for the Unmanned Aerial
Vehicle (UAV). Although legal frameworks have emerged to govern urban air
spaces, their full integration into the decision process of autonomous agents
and operators remains an open task. In this work we present ProMis, a system
architecture for probabilistic mission design. It links the data available from
various static and dynamic data sources with legal text and operator
requirements by following principles of formal verification and probabilistic
modeling. Hereby, ProMis enables the combination of low-level perception and
high-level rules in AAM to infer validity over the UAV's state-space. To this
end, we employ Hybrid Probabilistic Logic Programs (HPLP) as a unifying,
intermediate representation between perception and action-taking. Furthermore,
we present methods to connect ProMis with crowd-sourced map data by generating
HPLP atoms that represent spatial relations in a probabilistic fashion. Our
claims of the utility and generality of ProMis are supported by experiments on
a diverse set of scenarios and a discussion of the computational demands
associated with probabilistic missions.
