论文标题
ASP中的冲突概括：学习正确有效的非地面约束
Conflict Generalisation in ASP: Learning Correct and Effective Non-Ground Constraints
论文作者
论文摘要
在解决一个问题实例时学到的概括和重复利用知识已被最新的答案集求解器忽略了。我们建议一种新的方法，该方法将概括性化以重新利用nogoods加速解决未来的问题实例。我们的解决方案将著名的ASP解决技术与基于演绎逻辑的机器学习相结合。通过将学习的非地面约束添加到原始程序中可以提高解决性能。我们通过现实的例子来证明我们方法的效果，表明我们的方法需要低计算成本才能学习约束，从而在我们的测试案例中产生显着的性能收益。这些好处可以通过地面和解决系统以及懒惰的系统来看待。但是，在某些情况下，地面和解决系统遭受了其他限制，这是由其他限制引起的。通过最小化的冲突，可以减少非最小程度的学习约束。正如我们的实验所表明的那样，这可能会导致基础和解决工作的大幅减少。 （正在考虑在TPLP中接受。）
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.)
