Towards compositional automated planning

Abstract

The development of efficient propositional satisfiability 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.

Evaluation of high level methods for efficient planning as satisfiability

“…Probably one of the biggest contributors to speed in planning as satisfiability is parallel planning and it is presented in detail in [20]. Moreover, there is compositional planning as investigated in [21], and hierarchical planning [22]. Big performance improvements in planning as satisfiability can be unlocked when methods are altered on the low, SAT solving level.…”