ASP and Pseudo-Boolean Benchmarks

该数据集包含了用于评估现代伪布尔求解器在答案集编程（ASP）模型上性能的基准家族。这些实例来自四个围绕特定图问题及其可满足性的基准家族。此外，数据集还包括了2017年ASP竞赛中的决策与优化问题，但排除了那些无法紧凑地转换为伪布尔理论的特定问题。实例以固定步长线性扩展。任务是对求解器在ASP模型上的性能进行评估，并将其与传统ASP求解器进行比较。

This dataset comprises benchmark families used to evaluate the performance of modern pseudo-Boolean solvers on Answer Set Programming (ASP) models. These instances originate from four benchmark families centered on specific graph problems and their satisfiability. Additionally, the dataset includes decision and optimization problems from the 2017 ASP Competition, excluding specific problems that cannot be compactly translated into pseudo-Boolean theories. The instances scale linearly with a fixed step size. The core task of this dataset is to evaluate the performance of solvers on ASP models and compare their performance against traditional ASP solvers.