Predicting optimal solution cost with bidirectional stratified sampling

Levi Lelis, Roni Stern, Ariel Felner, Sandra Zilles, Robert C. Holte

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Optimal planning and heuristic search systems solve state-space search problems by finding a least-cost path from start to goal. As a byproduct of having an optimal path they also determine the optimal solution cost. In this paper we focus on the problem of determining the optimal solution cost for a state-space search problem directly, i.e., without actually finding a solution path of that cost. We present an efficient algorithm, BiSS, based on ideas of bidirectional search and stratified sampling that produces accurate estimates of the optimal solution cost. Our method is guaranteed to return the optimal solution cost in the limit as the sample size goes to infinity. We show empirically that our method makes accurate predictions in several domains. In addition, we show that our method scales to state spaces much larger than can be solved optimally. In particular, we estimate the average solution cost for the 6x6, 7x7, and 8x8 Sliding-Tile Puzzle and provide indirect evidence that these estimates are accurate.

Original languageEnglish
Title of host publicationICAPS 2012 - Proceedings of the 22nd International Conference on Automated Planning and Scheduling
Pages155-163
Number of pages9
StatePublished - 2012
Externally publishedYes
Event22nd International Conference on Automated Planning and Scheduling, ICAPS 2012 - Atibaia, Sao Paulo, Brazil
Duration: 25 Jun 201229 Jun 2012

Publication series

NameICAPS 2012 - Proceedings of the 22nd International Conference on Automated Planning and Scheduling

Conference

Conference22nd International Conference on Automated Planning and Scheduling, ICAPS 2012
Country/TerritoryBrazil
CityAtibaia, Sao Paulo
Period25/06/1229/06/12

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