一种面向模型的答案集程序设计中对称提升方法
A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming
摘要：
在求解组合问题时，从搜索空间中修剪对称解候选是必不可少的。大多数现有的方法是特定于实例的，并专注于自动计算对称破缺约束（SBC）为每个给定的问题实例。然而，这种方法应用于大规模的实例或高级问题编码可能是有问题的，因为计算的SBC是命题的，因此，既不能被有意义地解释，也不能被转移到其他实例。因此，在每次调用求解器之前，必须对SBC进行耗时的重新计算。为了克服这些限制，我们引入了一种新的面向模型的方法，答案集编程，升降机的SBC小问题实例到一组可解释的一阶约束，使用一种形式的机器学习称为归纳逻辑程序设计。针对简单的组合问题后，我们的目标是扩展我们的方法也适用于先进的决策和优化问题。
英文摘要：
When solving combinatorial problems, pruning symmetric solution candidates from the search space is essential. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using a form of machine learning called Inductive Logic Programming. After targeting simple combinatorial problems, we aim to extend our method to be applied also for advanced decision and optimization problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
