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. Based on this observation
and the lack of good heuristics for planning formalisms with
expressive extensions, symbolic search turns out to be a
strong approach. We introduce symbolic search to support each
of the formalisms individually and in combination, resulting
in optimal, sound, and complete planning algorithms that
empirically compare favorably with other approaches.