一句话：提出神经符号框架 NeuroStrata，用于提升自主 CPS 的可测试性与可验证性。
自主信息物理系统（CPS）将 AI 用于感知、规划与控制，但不确定性使其难以获得信任并通过安全认证。神经符号范式用可解释的符号 AI 替代随机层，有望带来确定性，但在多传感器融合、适应性和验证方面仍面临挑战。本文提出 NeuroStrata——一个面向自主 CPS 的神经符号框架，旨在增强其测试与验证能力。论文概述框架的关键组件，给出初步结果，并阐述未来研究计划。
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.
@inproceedings{paperbot9152,
title = {NeuroStrata: Harnessing Neuro-Symbolic Paradigms for Improved Testability and Verifiability of Autonomous CPS},
author = {Xi Zheng and Ziyang Li and Ivan Ruchkin and Ruzica Piskac and Miroslav Pajic},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3728500}
}