Exciting contemporary machine learning problems have recently been phrased in the classic formalism of tree search -- most famously, the game of Go. Interestingly, the state-space underlying these sequential decision-making problems often posses a more general latent structure than can be captured by a tree. In this work, we develop a probabilistic framework to exploit a search space's latent structure and thereby share information across the search tree. The method is based on a combination of approximate inference in jointly Gaussian models for the explored part of the problem, and an abstraction for the unexplored part that imposes a reduction of complexity ad hoc. We empirically find our algorithm to compare favorably to existing non-probabilistic alternatives in Tic-Tac-Toe and a feature selection application.
翻译:最近,在典型的树类搜索形式主义 -- -- 最著名的“Go”游戏 -- -- 中,描述了当代机器学习问题。有趣的是,这些相继决策问题背后的国家空间往往拥有比一棵树所能捕捉到的更一般的潜在结构。在这项工作中,我们开发了一个概率框架来利用搜索空间的潜伏结构,从而在搜索树上共享信息。这个方法基于对问题探索部分的Gaussian联合模型的近似推论和对未探索部分的抽象推理,而未探索部分则导致复杂性的减少。我们从经验上发现我们的算法可以比得上Tic-Tac-Toe中现有的非概率替代方法以及特征选择应用程序。