Structural Patterns Heuristics via Fork Decomposition

We consider a generalization of the PDB homomorphism abstractions to what is called "structural patterns". The bais in abstracting the problem in hand into provably tractable fragments of optimal planning, alleviating by that the constraint of PDBs to use projections of only low dimensionality. We introduce a general framework for additive structural patterns based on decomposing the problem along its causal graph, suggest a concrete non-parametric instance of this framework called fork-decomposition, and formally show that the admissible heuristics induced by the latter abstractions provide state-of-the-art worst-case informativeness guarantees on several standard domains.

• Type: Conference
• Year: 2008
• Where: AIPS
• Authors: Michael Katz, Carmel Domshlak
• Keywords: Additive Structural Patterns, AIPS 2008, Artificial Intelligence, PDB Homomorphism Abstractions, Structural Patterns