Abstract
Classical planning is concerned with problems where a goal needs to be reached
from a known initial state by doing actions with deterministic, known effects.
Classical planners, however, deal only with classical problems that can be
expressed in declarative planning languages such as STRIPS or PDDL. This
prevents their use on problems that are not easy to model declaratively or
whose dynamics are given via simulations. Simulators do not provide a
declarative representation of actions, but simply return successor states.
The question we address in this paper is: can a planner that has access to
the structure of states and goals only, approach the performance of planners
that also have access to the structure of actions expressed in PDDL?
To answer this, we develop domain-independent, black box planning algorithms
that completely ignore action structure, and show that they match the
performance of state-of-the-art classical planners on the standard planning
benchmarks. Effective black box algorithms open up new possibilities for
modeling and for expressing control knowledge, which we also illustrate.
Keywords
- Planning and Scheduling: Planning Algorithms
- Planning and Scheduling: Search in Planning and Scheduling
