ASP中的冲突泛化：学习正确有效的非基础约束
Conflict Generalisation in ASP: Learning Correct and Effective Non-Ground Constraints
摘要：
最先进的答案集求解器忽视了在解决一个问题实例时概括和重用学到的知识。我们提出了一种新的方法，概括学习nogoods重复使用，以加快解决未来的问题实例。我们的解决方案结合了著名的ASP解决技术与基于演绎逻辑的机器学习。通过在原程序中加入学习到的非地面约束，可以提高求解性能。我们展示了我们的方法的效果，通过现实的例子，表明我们的方法需要低的计算成本来学习约束，在我们的测试用例中产生显着的性能优势。这些好处可以在接地和解决系统以及惰性接地系统中看到。然而，接地和解决系统遭受额外的接地开销，在某些情况下由额外的约束引起。通过冲突最小化，可以减少非最小学习约束。正如我们的实验所示，这可以显著减少接地和解决工作。（正在考虑接受TPLP。
英文摘要：
Generalising and re-using knowledge learned while solving one problem instance has been neglected by state-of-the-art answer set solvers. We suggest a new approach that generalises learned nogoods for re-use to speed-up the solving of future problem instances. Our solution combines well-known ASP solving techniques with deductive logic-based machine learning. Solving performance can be improved by adding learned non-ground constraints to the original program. We demonstrate the effects of our method by means of realistic examples, showing that our approach requires low computational cost to learn constraints that yield significant performance benefits in our test cases. These benefits can be seen with ground-and-solve systems as well as lazy-grounding systems. However, ground-and-solve systems suffer from additional grounding overheads, induced by the additional constraints in some cases. By means of conflict minimization, non-minimal learned constraints can be reduced. This can result in significant reductions of grounding and solving efforts, as our experiments show. (Under consideration for acceptance in TPLP.)
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。
