---
title: "Factored Planning Using Decomposition Trees"
authors:
  - "Elena Kelareva"
  - "Olivier Buffet"
  - "Jinbo Huang"
  - "Sylvie Thiébaux"
doi: ""
url: "https://ijcai.org/proceedings/2007/313"
pdf: "https://ijcai.org/proceedings/2007/0313.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1942-1946"
track: ""
keywords:
---

# Factored Planning Using Decomposition Trees

Elena Kelareva, Olivier Buffet, Jinbo Huang, Sylvie Thiébaux

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1942-1946.

## Abstract

Improving AI planning algorithms relies on the ability to exploit the structure of the problem at hand. A promising direction is that of factored planning, where the domain is partitioned into subdomains with as little interaction as possible. Recent work in this field has led to an detailed theoretical analysis of such approaches and to a couple of high-level planning algorithms, but with no practical implementations or with limited experimentations. This paper presents dTreePlan, a new generic factored planning algorithm which uses a decomposition tree to efficiently partition the domain. We discuss some of its aspects, progressively describing a specific implementation before presenting experimental results. This prototype algorithm is a promising contribution---with major possible improvements---and helps enrich the picture of factored planning approaches.