NeuroStrata: Harnessing Neuro-Symbolic Paradigms for Improved Testability and Verifiability of Autonomous CPS
Autonomous cyber-physical systems (CPSs) leverage AI for perception, planning, and control but face trust and safety certification challenges due to inherent uncertainties. The neurosymbolic paradigm replaces stochastic layers with interpretable symbolic AI, enabling determinism. While promising, challenges like multisensor fusion, adaptability, and verification remain. This paper introduces NeuroStrata, a neurosymbolic framework to enhance the testing and verification of autonomous CPS. We outline its key components, present early results, and detail future plans.
Authors
- Ivan Ruchkin (ORCID: https://orcid.org/0000-0003-3546-414X)
- Ružica Piskač (ORCID: https://orcid.org/0000-0002-3267-0776)
- Xi Zheng (ORCID: https://orcid.org/0000-0002-2572-2355)
- Miroslav Pajić (ORCID: https://orcid.org/0000-0002-5357-0117)
- Ziyang Li (ORCID: https://orcid.org/0000-0003-3925-9549)
Institutions
- Duke University (US)
- University of Florida (US)
- University of Pennsylvania (US)
- Philadelphia University (US)
- Macquarie University (AU)
Publication Details
- Published
- 2025-06-23
- DOI
- https://doi.org/10.1145/3696630.3728500
- Citations
- 1
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- National Science Foundation
- Australian Research Council