用蒙特卡洛树搜索优化贝叶斯网络结构学习，提升效率与准确性。
Enhancing Bayesian Network Structural Learning with Monte Carlo Tree Search
- 将蒙特卡洛树搜索用于贝叶斯网络变量顺序搜索，结合启发式算法生成初始路径。
- 在大规模网络中表现稳定，即使基础顺序不佳仍优于传统方法。
- 适合需要高精度结构学习的科研与工业场景，尤其适用于复杂数据建模。
本文提出MCTS-BN，将蒙特卡洛树搜索（MCTS）算法应用于贝叶斯网络（BNs）的结构学习。原为游戏树探索设计的MCTS被重新用于探索贝叶斯网络中潜在的变量祖先顺序。随后，利用爬山法（HC）从每个顺序推导出具体的网络结构。在大型贝叶斯网络中，变量顺序的搜索空间极为庞大，完全随机的回溯（rollout）过程不可靠且不切实际。为此，本文采用半随机策略，引入其他启发式算法（如贪婪等价搜索GES、PC或HC自身）生成的变量顺序作为初始路径，以减轻计算负担并提高回溯可靠性。实验表明，MCTS-BN能有效改进传统结构学习算法生成的贝叶斯网络，在基础顺序欠佳时仍表现稳健，并在获得良好初始顺序时超越现有基准。
原文摘要 · Abstract (English)
This article presents MCTS-BN, an adaptation of the Monte Carlo Tree Search (MCTS) algorithm for the structural learning of Bayesian Networks (BNs). Initially designed for game tree exploration, MCTS has been repurposed to address the challenge of learning BN structures by exploring the search space of potential ancestral orders in Bayesian Networks. Then, it employs Hill Climbing (HC) to derive a Bayesian Network structure from each order. In large BNs, where the search space for variable orders becomes vast, using completely random orders during the rollout phase is often unreliable and impractical. We adopt a semi-randomized approach to address this challenge by incorporating variable orders obtained from other heuristic search algorithms such as Greedy Equivalent Search (GES), PC, or HC itself. This hybrid strategy mitigates the computational burden and enhances the reliability of the rollout process. Experimental evaluations demonstrate the effectiveness of MCTS-BN in improving BNs generated by traditional structural learning algorithms, exhibiting robust performance even when base algorithm orders are suboptimal and surpassing the gold standard when provided with favorable orders.
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