Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
This addresses the efficiency of backtracking search algorithms for CSPs, which is a domain-specific problem in constraint programming, with incremental improvements over existing methods.
The paper tackled the problem of improving variable ordering heuristics for solving Constraint Satisfaction Problems (CSPs) by proposing a deep reinforcement learning approach, resulting in learned policies that outperform classical hand-crafted heuristics in minimizing search tree size and generalize to larger instances.
Backtracking search algorithms are often used to solve the Constraint Satisfaction Problem (CSP). The efficiency of backtracking search depends greatly on the variable ordering heuristics. Currently, the most commonly used heuristics are hand-crafted based on expert knowledge. In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances. We show that directly optimizing the search cost is hard for bootstrapping, and propose to optimize the expected cost of reaching a leaf node in the search tree. To capture the complex relations among the variables and constraints, we design a representation scheme based on Graph Neural Network that can process CSP instances with different sizes and constraint arities. Experimental results on random CSP instances show that the learned policies outperform classical hand-crafted heuristics in terms of minimizing the search tree size, and can effectively generalize to instances that are larger than those used in training.