The representation of a dynamic problem in ASP usually boils down to using copies of variables and constraints, one for each time stamp, no matter whether it is directly encoded or via an action or temporal language. The multiplication of variables and constraints is commonly done during grounding and the solver is completely ignorant about the temporal relationship among the different instances. On the other hand, a key factor in the performance of today's ASP solvers is conflict-driven constraint learning. Our question is now whether a constraint learned for particular time steps can be generalized and reused at other time stamps, and ultimately whether this enhances the overall solver performance on temporal problems. Knowing full well the domain of time, we study conditions under which learned dynamic constraints can be generalized. We propose a simple translation of the original logic program such that, for the translated programs, the learned constraints can be generalized to other time points. Additionally, we identify a property of temporal problems that allows us to generalize all learned constraints to all time steps. It turns out that this property is satisfied by many planning problems. Finally, we empirically evaluate the impact of adding the generalized constraints to an ASP solver

通过对时间点之间的关系进行研究，我们提出了一种新的逻辑程序翻译方法，该方法可以使学到的约束条件在不同的时间点进行泛化；同时，我们发现了一种可以将所有学到的约束条件泛化到所有时间点的时间问题属性，并通过对ASP求解器的实证评估，展示了将泛化约束条件添加到ASP求解器中所产生的影响。

关于在时态域中应用学习约束的推理扩展