Additive Pattern Databases for Decoupled Search

Silvan Sievers, Daniel Gnad, Alvaro Torralba

TL;DR: We investigate pattern database heuristics for decoupled search

Abstract:

Abstraction heuristics are the state of the art in optimal classical planning as
heuristic search. Despite their success for explicit-state search, though,
abstraction heuristics are not available for decoupled state-space search, an
orthogonal reduction technique that can lead to exponential savings by decomposing
planning tasks. In this paper, we show how to compute pattern database (PDB)
heuristics for decoupled states. The main challenge lies in how to additively employ
multiple patterns, which is crucial for strong search guidance of the heuristics. We
show that in the general case, for arbitrary collections of PDBs, computing the
heuristic for a decoupled state is exponential in the number of leaf components of
decoupled search. We derive several variants of decoupled PDB heuristics that allow
to additively combine PDBs avoiding this blow-up and evaluate them empirically.