TY - JOUR AU - Spieker, Helge PY - 2019/07/17 Y2 - 2024/03/28 TI - Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - Student Abstract Track DO - 10.1609/aaai.v33i01.330110037 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5155 SP - 10037-10038 AB - <p>This paper proposes a framework for solving constraint problems with reinforcement learning (RL) and sequence-tosequence recurrent neural networks. We approach constraint solving as a declarative machine learning problem, where for a variable-length input sequence a variable-length output sequence has to be predicted. Using randomly generated instances and the number of constraint violations as a reward function, a problem-specific RL agent is trained to solve the problem. The predicted solution candidate of the RL agent is verified and repaired by CBLS to ensure solutions, that satisfy the constraint model. We introduce the framework and its components and discuss early results and future applications.</p> ER -