  In classical planning, the goal is to derive a course of actions that allows
an intelligent agent to move from any situation it finds itself in to one that
satisfies its goals. Classical planning is considered domain-independent, i.e.,
it is not limited to a particular application and can be used to solve
different types of reasoning problems. In practice, however, some properties of
a planning problem at hand require an expressive extension of the standard
classical planning formalism to capture and model them. Although the importance
of many of these extensions is well known, most planners, especially optimal
planners, do not support these extended planning formalisms. The lack of
support not only limits the use of these planners for certain problems, but
even if it is possible to model the problems without these extensions, it often
leads to increased effort in modeling or makes modeling practically impossible
as the required problem encoding size increases exponentially.
  In this thesis, we propose to use symbolic search for cost-optimal planning
for different expressive extensions of classical planning, all capturing
different aspects of the problem. In particular, we study planning with axioms,
planning with state-dependent action costs, oversubscription planning, and
top-k planning. For all formalisms, we present complexity and compilability
results, highlighting that it is desirable and even necessary to natively
support the corresponding features. We analyze symbolic heuristic search and
show that the search performance does not always benefit from the use of a
heuristic and that the search performance can exponentially deteriorate even
under the best possible circumstances, namely the perfect heuristic. This
reinforces that symbolic blind search is the dominant symbolic search strategy
nowadays, on par with other state-of-the-art cost-optimal planning
strategies...
