Probabilistic Mission Design in Neuro-Symbolic Systems
This work addresses the problem of robust mission planning in dynamic human-inhabited environments for logistics and emergency response, representing an incremental advancement by extending prior reasoning capabilities with machine learning integrations.
The paper tackles the challenge of modeling legal concepts and uncertainties for Unmanned Aircraft Systems in Advanced Air Mobility by introducing Probabilistic Mission Design (ProMis), a neuro-symbolic architecture that generates Probabilistic Mission Landscapes to quantify belief in mission conditions across navigation spaces, integrating with Large Language Models and Transformer-based vision models for multi-modal applications.
Advanced Air Mobility (AAM) is a growing field that demands accurate modeling of legal concepts and restrictions in navigating intelligent vehicles. In addition, any implementation of AAM needs to face the challenges posed by inherently 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.