---
title: "Symbolic Merge-and-Shrink for Cost-Optimal Planning"
authors:
  - "Álvaro Torralba Arias de Reyna"
  - "Carlos Linares López"
  - "Daniel Borrajo"
doi: ""
url: "https://ijcai.org/proceedings/2013/352"
pdf: "https://ijcai.org/proceedings/2013/0352.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "2394-2399"
track: ""
keywords:
---

# Symbolic Merge-and-Shrink for Cost-Optimal Planning

Álvaro Torralba Arias de Reyna, Carlos Linares López, Daniel Borrajo

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 2394-2399.

## Abstract

Symbolic PDBs and Merge-and-Shrink (M&S) are two approaches to derive admissible heuristics for optimal planning. We present a combination of these techniques, Symbolic Merge-and-Shrink (SM&S), which uses M&S abstractions as a relaxation criterion for a symbolic backward search. Empirical evaluation shows that SM&S has the strengths of both techniques deriving heuristics at least as good as the best of them for most domains.