Factored planning using decomposition trees

Authors:
Elena Kelareva;Olivier Buffet;Jinbo Huang;Sylvie Thiébaux
Affiliations:
University of Melbourne, Melbourne, Victoria, Australia;LAAS-CNRS, France and National ICT Australia and Australian National University, Canberra, ACT, Australia;National ICT Australia and Australian National University, Canberra, ACT, Australia;National ICT Australia and Australian National University, Canberra, ACT, Australia
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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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.