Publikation
Probabilistic Mission Design in Neuro-Symbolic Systems
Simon Kohaut; Benedict Flade; Daniel Ochs; Devendra Singh Dhami; Julian Eggert; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2501.01439, Pages 1-10, Computing Research Repository, 2025.
Zusammenfassung
Advanced Air Mobility (AAM) is a growing field
that demands accurate modeling of legal concepts and restrictions
in navigating intelligent vehicles. In addition, any implemen-
tation of AAM needs to face the challenges posed by inher-
ently dynamic and uncertain human-inhabited spaces robustly.
Nevertheless, the employment of Unmanned Aircraft Systems
(UAS) beyond visual line of sight (BVLOS) is an endearing
task that promises to enhance significantly today’s logistics and
emergency response capabilities. To tackle these challenges, we
present a probabilistic and neuro-symbolic architecture to encode
legal frameworks and expert knowledge over uncertain spatial
relations and noisy perception in an interpretable and adaptable
fashion. More specifically, we demonstrate Probabilistic Mission
Design (ProMis), a system architecture that links geospatial
and sensory data with declarative, Hybrid Probabilistic Logic
Programs (HPLP) to reason over the agent’s state space and
its legality. As a result, ProMis generates Probabilistic Mission
Landscapes (PML), which quantify the agent’s belief that a set
of mission conditions is satisfied across its navigation space.
Extending prior work on ProMis’ reasoning capabilities and
computational characteristics, we show its integration with potent
machine learning models such as Large Language Models (LLM)
and Transformer-based vision models. Hence, our experiments
underpin the application of ProMis with multi-modal input data
and how our method applies to many important AAM scenarios.
Index Terms—Probabilistic Mission Design, Advanced Air
Mobility, Neuro-Symbolic Systems