Project Description (150 words)

Methods for injecting constraints in Machine Learning (ML) can help bridging the gap between symbolic and subsymbolic models, and address fairness and safety issues in data-driven AI systems. The recently proposed Moving Targets approach achieves this via a decomposition, where a classical ML model deals with the data and a separate constraint solver with the constraints.

Different applications call for different constraints, solvers, and ML models: this flexibility is a strength of the approach, but it makes it also difficult to set up and analyze.

Therefore, this project will rely on the AI Domain Definition Language (AIDDL) framework to obtain a flexible implementation of the approach, making it simpler to use and allowing the exploration of more case studies, different constraint solvers, and algorithmic variants. We will use this implementation to investigate various new constraint types integrated with the Moving Targets approach (e.g. SMT, MINLP, CP).

Output

Stand-alone moving targets system distributed via the AI4EU platform

Interactive tutorial to be available on the AI4EU platform

Scientific paper discussing the outcome of our evaluation and the resulting system

Project Partners:

  • Örebro University (ORU), Uwe Köckemann
  • Università di Bologna (UNIBO), Michele Lombardi

Primary Contact: Uwe Köckemann, Örebro University