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).
Members Bart Bogaerts
In collaboration with Tias Guns (KU Leuven)