论文标题
迈向流线型约束模型的投资组合：一个具有平衡学术课程问题的案例研究
Towards Portfolios of Streamlined Constraint Models: A Case Study with the Balanced Academic Curriculum Problem
论文作者
论文摘要
使用其他约束来增强基本约束模型可以增强求解器的推论，从而减少搜索工作。我们专注于自动添加流媒体约束，这些约束源自对问题的兴趣类别的抽象本质规范中存在的类型，这些类型的兴趣类别的交易完整性可能会非常明显地减少搜索。除了基本本质规范所需的内容之外，精简的本质规范的细化中的细化规格不适合输入限制求解器的约束模型。以前的自动化方法在仅评估每个流线型规范的单个默认模型时受到限制。在本文中，我们探讨了模型选择在简化规范的上下文中的效果。我们提出了一种新的最佳优先搜索方法，该方法通过评估每个Streamliner的模型组合来搜索和探索性能变异性并找到最佳模型，从而生成Pareto最佳Streamliner模型组合组合的组合。各种形式的赛车用于限制培训的计算成本。
Augmenting a base constraint model with additional constraints can strengthen the inferences made by a solver and therefore reduce search effort. We focus on the automatic addition of streamliner constraints, derived from the types present in an abstract Essence specification of a problem class of interest, which trade completeness for potentially very significant reduction in search. The refinement of streamlined Essence specifications into constraint models suitable for input to constraint solvers gives rise to a large number of modelling choices in addition to those required for the base Essence specification. Previous automated streamlining approaches have been limited in evaluating only a single default model for each streamlined specification. In this paper we explore the effect of model selection in the context of streamlined specifications. We propose a new best-first search method that generates a portfolio of Pareto Optimal streamliner-model combinations by evaluating for each streamliner a portfolio of models to search and explore the variability in performance and find the optimal model. Various forms of racing are utilised to constrain the computational cost of training.
