Towards compositional automated planning

Paper i proceeding, 2020

The development of efficient propositional satisfi-
ability problem solving algorithms (SAT solvers) in the past
two decades has made automated planning using SAT-solvers
an established AI planning approach. Modern SAT solvers can
accommodate a wide variety of planning problems with a large
number of variables. However, fast computing of reasonably long
plans proves challenging for planning as satisfiability. In order
to address this challenge, we present a compositional approach
based on abstraction refinement that iteratively generates, solves
and composes partial solutions from a parameterized planning
problem. We show that this approach decomposes the monolithic
planning problem into smaller problems and thus significantly
speeds up plan calculation, at least for a class of tested planning
problems.