Knowledge Extraction, Machine Learning and other AI approaches for secure, robust, frugal and explainable solutions in Defence Applications

The project “Knowledge Extraction, Machine Learning and other AIapproaches for secure, robust, frugal, resilient and explainable solutions in Defence Applications” (KOIOS) will be developed around four main outputs (Simulation framework with performance computing capabilities and reduced complexity; Identification and development of use cases; Definition of ametrics specifically designed for measuring frugality, robustness, resilience; and Development of experiments built on a common protocol ensuring their reproducibility) for enhanced AIusagein defence applications.

Goal
The main objective of our project, KOIOS, is to develop new AI-based methods that are trustworthy (under human control and explainable), robust (resilient to attacks) and frugal in the use of resources (data, computing capabilities, energy,…) addressing tasks in a more efficient manner than the current state-of-the-art ML/DL methods while maintaining similar performance, and improving resilience (for adversarial attacks), consistent behaviour and limiting the cognitive and technical efforts when adapting to new data or dynamic contexts.

Lead Partner
CT INGENIEROS AERONAUTICOS DE AUTOMOCION E INDUSTRIALES SL
Partners involved
BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACION, TOTALFORSVARETS FORSKNINGSINSTITUT, UNIVERSITAET DER BUNDESWEHR MUENCHEN, NTT DATA SPAIN, SL, OFFICE NATIONAL D’ETUDES ET DE RECHERCHES AEROSPATIALES, LABORATOIRE NATIONAL DE METROLOGIE ET D’ESSAIS, MITIGA SOLUTIONS SL, APPLIED INTELLIGENCE ANALYTICS LIMITED, TECHNISCHE UNIVERSITEIT EINDHOVEN, STICHTING MARITIEM RESEARCH INSTITUUT NEDERLAND, IDRYMA TECHNOLOGIAS KAI EREVNAS, UNIVERSITA’ DEGLI STUDI DI BERGAMO, VOCAPIA RESEARCH

Duration
01 December 2022 – 30 November 2025

Funding
European Defence Fund

More information
https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/projects-details/44181033/101103770/EDF
https://edrin.org/fileadmin/media/Factsheet_EDF21_KOIOS.pdf