EU - FOCETA - Foundations for continuous engineering of trustworthy autonomy

Project: Research project

Project Details

Description

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.
StatusFinished
Effective start/end date1/10/2030/09/23

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  • Analyzing Intentional Behavior in Autonomous Agents Under Uncertainty

    Cano Córdoba, F., Judson, S., Antonopoulos, T., Bjørner, K., Shoemaker, N., Shapiro, S., Piskac, R. & Könighofer, B., Aug 2023, Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. ijcai.org, p. 372--381 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

    Open Access
  • Formal XAI via Syntax-Guided Synthesis

    Bjørner, K., Judson, S., Cano, F., Goldman, D., Shoemaker, N., Piskac, R. & Könighofer, B., 2023, Bridging the Gap Between AI and Reality : First International Conference, AISoLA 2023, Crete, Greece, October 23–28, 2023, Proceedings. Springer, p. 119-137 (Lecture Notes in Computer Science; vol. 14380).

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

  • Provable Correct and Adaptive Simplex Architecture for Bounded-Liveness Properties

    Maderbacher, B., Schupp, S., Bartocci, E., Bloem, R., Nickovic, D. & Könighofer, B., May 2023, Model Checking Software - 29th International Symposium, SPIN 2023, Proceedings. Caltais, G. & Schilling, C. (eds.). Springer Nature Switzerland AG, p. 141-160 20 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13872 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

    Open Access
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