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.

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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

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article

NeuroStrata: Harnessing Neuro-Symbolic Paradigms for Improved Testability and Verifiability of Autonomous CPS

Ivan Ruchkin, Ružica Piskač, Xi Zheng, Miroslav Pajić et al.
1 citations
Adversarial Robustness in Machine Learning
2.48
article

NeuroStrata: Harnessing Neuro-Symbolic Paradigms for Improved Testability and Verifiability of Autonomous CPS

Ivan Ruchkin, Ružica Piskač, Xi Zheng, Miroslav Pajić, Ziyang Li
article en
1 citations

Abstract

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.

Duke University (US), University of Florida (US), University of Pennsylvania (US), Philadelphia University (US), Macquarie University (AU)
National Science Foundation, Australian Research Council
Openalex Percentile: Top 9%
Adversarial Robustness in Machine Learning
2.48
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