Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Finding Diverse Solutions of High Quality to Constraint Optimization Problems / 260
Thierry Petit, Andrew C. Trapp

A number of effective techniques for constraint-based optimization can be used to generate either diverse or high-quality solutions independently, but no framework is devoted to accomplish both simultaneously. In this paper, we tackle this issue with a generic paradigm that can be implemented in most existing solvers. We show that our technique can be specialized to produce diverse solutions of high quality in the context of over-constrained problems. Furthermore, our paradigm allows us to consider diversity from a different point of view, based on generic concepts expressed by global constraints.