Foundations For Continuous Engineering Of Trustworthy Autonomy (at Liverpool)
Objective: Ubiquitous AI will soon allow complex systems to drive on our roads, fly over our heads, move alongside us during our daily lives & work in our factories. In spite of this disruptive landscape, deployment and broader adoption of learned-enabled autonomous systems in safety-critical scenarios remains challenging. Continuous engineering (DevOps) can mediate problems when encountering new scenarios throughout the product life cycle. However, the technical foundations and assumptions on which traditional safety engineering principles rely do not extend to learning-enabled autonomous systems engineered under continuous development.
FOCETA gathers prominent academic groups & leading industrial partners to develop foundations for continuous engineering of trustworthy learning-enabled autonomous systems. The targeted scientific breakthrough lies within the convergence of “data-driven” and “model-based” engineering, where this convergence is further complicated by the need to apply verification and validation incrementally & avoid complete re-verification & re-validation efforts.
FOCETA’s paradigm is built on three scientific pillars: (1) integration of learning-enabled components & model-based components via a contract-based methodology which allows incremental modification of systems including threat models for cyber-security, (2) adaptation of verification techniques applied during model-driven design to learning components in order to enable unbiased decision making, & finally, (3) incremental synthesis techniques unifying both the enforcement of safety & security-critical properties as well as the optimization of performance.
FOCETA approach, implemented in open source tools & with open data exchange standards, will be applied to the most demanding & challenging applications such as urban driving automation & intelligent medical devices, to demonstrate its viability, scalability & robustness, while addressing European industry cutting-edge technology needs.
Funding Agency: EU H2020 link
Project Time: 2020 - 2023
- Xiaowei Huang (Liverpool lead)
- Sven Schewe (Liverpool Co-I)
- Xingyu Zhao (postdoc)
- (Project PI, UGA Verimag (UGA))
- (Graz University of Technology)
- (Bar Ilan University)
- (Aristotle University of Thessaloniki)
- (RGB Medical)
- (L-UP SAS)
Please feel free to contact me for more information.
- How does Weight Correlation Affect theGeneralisation Ability of Deep Neural Networks?
- Gaojie Jin, Xinping Yi, Liang Zhang, Lijun Zhang, Sven Schewe, Xiaowei Huang.
- NeurIPS 2020.
- arXiv version