Probabilistic Mission Design in Neuro-Symbolic Systems

• URL: http://arxiv.org/abs/2501.01439v1
• Date: Wed, 25 Dec 2024 11:04:00 GMT
• Title: Probabilistic Mission Design in Neuro-Symbolic Systems
• Authors: Simon Kohaut, Benedict Flade, Daniel Ochs, Devendra Singh Dhami, Julian Eggert, Kristian Kersting,
• Abstract summary: Probabilistic Mission Design (ProMis) is a system architecture that links geospatial and sensory data with declarative, Hybrid Probabilistic Logic Programs (HPLP)<n>ProMis generates Probabilistic Mission Landscapes (PML), which quantify the agent's belief that a set of mission conditions is satisfied across its navigation space.<n>We show its integration with potent machine learning models such as Large Language Models (LLM) and Transformer-based vision models.
• Score: 19.501311018760177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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