

A Case Study on Constrained Machine Learning
The tangible objective of this micro-project is develop a modular implementation in AIDDL of the Moving Targets algorithm, for injecting constraints in ML mode.
The moving targets method integrates machine learning and constraint optimization to enforce constraints on a machine learning model. The AI Domain Definition Language (AIDDL) provides a modeling language and framework for integrative AI.
We have implemented the moving targets algorithm in the AIDDL framework for integrative AI. This has benefits for modeling, experimentation, and usability. On the modeling side, this enables us to provide applications of "moving target" as regular machine learning problems extended with constraints and a loss function. On the experimentation side, we can now easily switch the learning and constraint solvers used by the "moving targets" algorithm, and we have added support for multiple constraint types. Finally, we made the "moving targets" method easier to use, since it can now be controlled through a small model written in the AIDDL language.
This micro-project was joinly performed by Uwe Köckemann, Fabrizio Detassis and Michele Lombardi. The project is part of the Humane-AI-Net network of excellent research centers in AI. It contributes to this network in the following respects:
Tangible outcomes:
A tutorial on the Moving Targets algorithm for constrained Machine Learning
A Python library that integrates machine learning with constraint optimization using the AI Domain Definition Language.