Aeros(CASP): Advancing Certification and Safety Through Commonsense Reasoning-Based Constraint Answer Set Programming

The grounding of the Boeing 737 Max underscores an urgent need for faster, more reliable, and scalable quality assurance processes in aerospace engineering. This paper introduces Aeros(CASP), a novel logic-based artificial intelligence framework designed to revolutionize automated airworthiness certification by ensuring formal correctness, reliability, and compliance throughout the certification process. Built on the Prolog-based non-monotonic reasoner s(CASP), Aeros(CASP) leverages constraint answer set programming to deliver deterministic, real-time validation of Federal Aviation Administration (FAA) standards. By encoding and verifying immutable regulatory rules with precision, Aeros(CASP) ensures rigorous alignment with federal requirements, significantly reducing the risks of human oversight and the inconsistencies of existing automated approaches. Unlike traditional certification methods or generative AI models prone to inaccuracies and ambiguities, Aeros(CASP) integrates rule-based reasoning with real-time compliance checks, offering a transparent and formally verifiable solution. Preliminary validation of the framework has demonstrated its ability to evaluate FAA compliance cases with clarity and consistency, addressing bottlenecks in traditional certification processes and setting a foundation for future scalability. Developed in alignment with DARPA’s Automated Rapid Certification of Software (ARCOS) initiative, Aeros(CASP) expands automation capabilities beyond software to encompass hardware systems, bridging a critical gap in modern aerospace certification. This paper outlines the technical architecture of Aeros(CASP), including its constraint-solving methodology, integration with existing regulatory workflows, and approaches to mitigating the challenges of applying formal logic systems to dynamic and complex aerospace requirements. By preserving human-centric control while enhancing safety and efficiency, Aeros(CASP) represents a transformative step forward in automated certification. Future research will explore its scalability to high-stakes domains such as autonomous vehicles and medical devices, highlighting its broader potential to redefine quality assurance in safety-critical industries.