We present a very general approach to learning the structure of causal models
based on d-separation constraints, obtained from any given set of overlapping
passive observational or experimental data sets. The procedure allows for both
directed cycles (feedback loops) and the presence of latent variables. Our
approach is based on a logical representation of causal pathways, which permits
the integration of quite general background knowledge, and inference is
performed using a Boolean satisfiability (SAT) solver. The procedure is
complete in that it exhausts the available information on whether any given
edge can be determined to be present or absent, and returns "unknown"
otherwise. Many existing constraint-based causal discovery algorithms can be
seen as special cases, tailored to circumstances in which one or more
restricting assumptions apply. Simulations illustrate the effect of these
assumptions on discovery and how the present algorithm scales.

本文提出了一种非常通用的方法来学习因果模型的结构，该方法基于从任何给定的重叠的被动观察或实验性数据集获得的 d - 分离约束。此方法允许直接循环（反馈回路）和潜在变量的存在。我们的方法基于因果路径的逻辑表示，允许将相当通用的背景知识集成，推理是使用布尔满足（SAT）求解器执行的。该过程是完整的，因为它用尽了关于是否可以确定存在或不存在任何给定边缘的可用信息，否则返回 “未知”。许多现有的基于约束的因果发现算法可以看作是特殊情况，适用于一个或多个限制性假设的情况。模拟说明了这些假设对发现的影响以及现有算法的扩展能力。

使用潜变量发现循环因果模型：一般的基于 SAT 算法的过程

Discovering Cyclic Causal Models with Latent Variables: A General  SAT-Based Procedure

We present and evaluate new techniques for designing algorithm portfolios. In
our view, the problem has both a scheduling aspect and a machine learning
aspect. Prior work has largely addressed one of the two aspects in isolation.
Building on recent work on the scheduling aspect of the problem, we present a
technique that addresses both aspects simultaneously and has attractive
theoretical guarantees. Experimentally, we show that this technique can be used
to improve the performance of state-of-the-art algorithms for Boolean
satisfiability, zero-one integer programming, and A.I. planning.

本文针对设计算法组合的问题，提出了并行考虑调度问题和机器学习问题的技术，并在布尔可满足性、01 整数规划和人工智能规划等领域取得了显著的实验性能提升，具有较强的理论保证。

算法组合设计新技术

New Techniques for Algorithm Portfolio Design

Maximum Satisfiability (MaxSAT) is a well-known optimization pro- blem, with
several practical applications. The most widely known MAXS AT algorithms are
ineffective at solving hard problems instances from practical application
domains. Recent work proposed using efficient Boolean Satisfiability (SAT)
solvers for solving the MaxSAT problem, based on identifying and eliminating
unsatisfiable subformulas. However, these algorithms do not scale in practice.
This paper analyzes existing MaxSAT algorithms based on unsatisfiable
subformula identification. Moreover, the paper proposes a number of key
optimizations to these MaxSAT algorithms and a new alternative algorithm. The
proposed optimizations and the new algorithm provide significant performance
improvements on MaxSAT instances from practical applications. Moreover, the
efficiency of the new generation of unsatisfiability-based MaxSAT solvers
becomes effectively indexed to the ability of modern SAT solvers to proving
unsatisfiability and identifying unsatisfiable subformulas.

本研究分析了现有的基于不可满足子公式识别的最大可满足性问题（MaxSAT）算法，并提出了几个关键性优化和新的替代算法，这些优化和新算法在实际应用中的 MaxSAT 实例上提供了显着的性能提升。