  The definition and representation of planning problems is at the heart of AI
planning research. A key part is the representation of action models. Decades
of advances improving declarative action model representations resulted in
numerous theoretical advances, and capable, working, domain-independent
planners. However, despite the maturity of the field, AI planning technology is
still rarely used outside the research community, suggesting that current
representations fail to capture real-world requirements, such as utilizing
complex mathematical functions and models learned from data. We argue that this
is because the modeling process is assumed to have taken place and completed
prior to the planning process, i.e., offline modeling for offline planning.
There are several challenges inherent to this approach, including: limited
expressiveness of declarative modeling languages; early commitment to modeling
choices and computation, that preclude using the most appropriate resolution
for each action model -- which can only be known during planning; and
difficulty in reliably using non-declarative, learned, models.
  We therefore suggest to change the AI planning process, such that is carries
out online modeling in offline planning, i.e., the use of action models that
are computed or even generated as part of the planning process, as they are
accessed. This generalizes the existing approach (offline modeling). The
proposed definition admits novel planning processes, and we suggest one
concrete implementation, demonstrating the approach. We sketch initial results
that were obtained as part of a first attempt to follow this approach by
planning with action cost estimators. We conclude by discussing open
challenges.
