---
title: "Plan Quality Optimisation via Block Decomposition"
authors:
  - "Fazlul Hasan Siddiqui"
  - "Patrik Haslum"
doi: ""
url: "https://ijcai.org/proceedings/2013/351"
pdf: "https://ijcai.org/proceedings/2013/0351.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "2387-2392"
track: ""
keywords:
---

# Plan Quality Optimisation via Block Decomposition

Fazlul Hasan Siddiqui, Patrik Haslum

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 2387-2392.

## Abstract

AI planners have to compromise between the speed of the planning process and the quality of the generated plan. Anytime planners try to balance these objectives by finding plans of better quality over time, but current anytime planners often do not make effective use of increasing runtime beyond a certain limit. We present a new method of continuing plan improvement, that works by repeatedly decomposing a given plan into subplans and optimising each subplan locally. The decomposition exploits block-structured plan deordering to identify coherent subplans, which make sense to treat as units. This approach extends the anytime capability of current planners - to provide continuing plan quality improvement at any time scale.