文献1：Applying UCT to Boolean Satisfiability
文献2：Monte-Carlo Style UCT Search for Boolean Satisfiability
文献3：
文献1——简要说明。没有介绍基本概念。
| UCT repeatedly starts from the root node and incrementally builds a tree based on estimates of node utilities and node visit frequencies computed from previous iterations. 译文：UCT重复地从根节点开始，并根据对节点实用程序的估计和从以前的迭代中计算出的节点访问频率逐步构建树。 In most implementations of UCT, the estimated utility of a new node is computed using Monte-Carlo methods, i.e., by generating random completions of the search (termed “playouts”) and averaging their outcomes. 译文：在大多数UCT实现中，新节点的估计效用是使用蒙特卡罗方法计算的，即，通过生成搜索的随机补全(称为“playouts”)并平均它们的结果。 This utility is revised each time the search revisits the node using the estimated values of the children. This technique is especially effective when no adequate heuristic is available to perform this value estimation task. 译文：每次搜索重新访问节点时，都会使用子节点的估计值对该实用程序进行修改。当没有足够的启发式来执行此值估计任务时，该技术尤其有效。 |
| In this paper, we introduce and study an algorithm called UCTSAT that employs the UCT search control mechanism but replaces the playouts with a heuristic to estimate the initial utility of a node.译文：在本文中，我们介绍并研究了一种名为UCTSAT的算法，该算法使用UCT搜索控制机制，但用启发式代替游戏规则来估计节点的初始效用。 The heuristic we use is the fraction of the total set of clauses that are satisfied by the partial assignment associated with the node; this fraction is computed after the application of unit propagation.译文：我们使用的启发式是与节点关联的部分分配所满足的子句总数的一部分;这个分数是在应用单位传播后计算的。 While we do not expect UCTSAT to outperform the highly-optimized, state of the art SAT solvers (especially with respect to CPU time), we believe that the development of an algorithm based on a radically different search technique is important for at least two reasons: (a) the hardness of SAT instances is related to the algorithm used [1], and hence UCTSAT, which uses a different search strategy, can provide useful and new insights into the complexity of SAT instances; and (b) because such an algorithm can be useful when included in a portfolio of algorithms (see, for example, [6]) where very different solution techniques can help expand the range of applicability of the portfolio. 译文：虽然我们不期望UCTSAT能超过高度优化的、最新的SAT求解器(特别是在CPU时间方面)，但我们相信基于完全不同的搜索技术的算法的发展至少有两个原因:
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| As such, we focus our efforts on understanding whether UCTSAT is capable of solving SAT instances using smaller search trees than DPLL.To simplify the comparisons, we contrast our algorithm against a no-frills implementation of DPLL.译文：因此，我们将精力集中在理解UCTSAT是否能够使用比DPLL更小的搜索树来解决SAT实例。我们将我们的算法与DPLL进行了简要比较。 We set the exploration bias parameter in UCTSAT to 0 as this yielded the best performance on average.译文：我们将UCTSAT中的勘探偏差参数设置为0，因为这平均能产生最佳性能。 We also experimented with varying the number of atoms that UCTSAT assigned at a given node in the search tree and discovered that setting more than one atom at once hurt the performance of the algorithm.译文：我们还尝试改变UCTSAT在搜索树中给定节点上分配的原子数量，发现一次设置多个原子会降低算法的性能。 |
| On uniform random 3-SAT and flat-graph coloring instances of various sizes, we found little difference in the sizes of the search trees constructed by the two algorithms. 译文：在不同大小的均匀随机3-SAT和平图着色实例上，我们发现两种算法所构建的搜索树的大小差别不大。 We believe that this is due to the unstructured nature of these instances — UCTSAT works well when each exploration of the tree yields information that can be successfully used in subsequent iterations. 译文：我们认为，这是由于这些实例的非结构化本质——UCTSAT在每次对树的探索产生可以在后续迭代中成功使用的信息时工作得很好。 In instances drawn from real-world problems (namely, single-stuckat-fault analysis problems) that exhibit structure, we discovered that UCTSAT constructs significantly smaller search trees than DPLL — this is illustrated in table 1. 译文：在展示结构的现实问题(即单stucka -fault分析问题)中，我们发现UCTSAT构建的搜索树要比DPLL小得多——如表1所示。 |
| Table 1. Average tree sizes (number of nodes) for SSA circuit fault analysis instances
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文献2 Monte-Carlo Style UCT Search for Boolean Satisfiability