TY - GEN

T1 - Predicting optimal solution cost with bidirectional stratified sampling

AU - Lelis, Levi

AU - Stern, Roni

AU - Felner, Ariel

AU - Zilles, Sandra

AU - Holte, Robert C.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84866443127&partnerID=8YFLogxK

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AN - SCOPUS:84866443127

SN - 9781577355625

T3 - ICAPS 2012 - Proceedings of the 22nd International Conference on Automated Planning and Scheduling

SP - 155

EP - 163

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

T2 - 22nd International Conference on Automated Planning and Scheduling, ICAPS 2012

Y2 - 25 June 2012 through 29 June 2012

ER -