FRESCO: A FRamework for Explainable Solving and Constraint Optimization

Development of a set of methods to make constraint solvers and optimizers explainable, i.e. allow them to explain their conclusions to humans.

As artificial intelligence (AI) tools employ more advanced reasoning mechanisms and computation, it becomes increasingly difficult to understand why certain decisions are made. Explainable AI research aims to fulfill the need for trustworthy AI systems that can explain their reasoning in a human-understandable way. Our
contribution to explainable AI is situated in the domain of constraint solving and optimization, where we aim to augment constraint solvers with explainable agency.

Based on research questions that came out of a preliminary study, the high-level objective of this research
project is to design an integrated framework for explainable constraint satisfaction and optimization. Developing such a framework comes with several questions, related to scalability (the ability to explain large instances), generality (the ability to answer different types of questions) and interactability (the ability to interact in a natural and fluent way with a user).


Project Info

Start 01/01/2021

End 31/12/2024

Funding FWO

Members Bart Bogaerts

In collaboration with Tias Guns (KU Leuven)