New AI Techniques for Combinatorial Optimization

Abstract

Combinatorial Optimization lies at the heart of many Artificial Intelligence tasks. While existing techniques are still improved year after year, we advocate that recent sub-symbolic approaches may change the game if they can be successfully integrated into combinatorial solvers. In this paper, we present a state of the art of current approaches and discuss the role of new learning techniques to improve the current results.