  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
