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Estimating Search Tree Size
- In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI ’06
, 2006
"... We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronologi-cal backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree will be simil ..."
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Cited by 26 (2 self)
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We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronologi-cal backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree will be similar to the part we have so far explored. We compare these methods against an old method due to Knuth based on random probing. We show that these methods can reliably estimate the size of search trees explored by both optimiza-tion and decision procedures. We also demonstrate that these methods for estimating search tree size can be used to select the algorithm likely to perform best on a particular problem instance.
Online Estimation of SAT Solving Runtime ⋆
"... Abstract. We present an online method for estimating the cost of solving SAT problems. Modern SAT solvers present several challenges to estimate search cost including non-chronological backtracking, learning and restarts. Our method uses a linear model trained on data gathered at the start of search ..."
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Cited by 5 (1 self)
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Abstract. We present an online method for estimating the cost of solving SAT problems. Modern SAT solvers present several challenges to estimate search cost including non-chronological backtracking, learning and restarts. Our method uses a linear model trained on data gathered at the start of search. We show the effectiveness of this method using random and structured problems. We demonstrate that predictions made in early restarts can be used to improve later predictions. We also show that we can use such cost estimations to select a solver from a portfolio. 1
Abstract Estimating Search Tree Size
"... We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronological backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree will be simila ..."
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We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronological backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree will be similar to the part we have so far explored. We compare these methods against an old method due to Knuth based on random probing. We show that these methods can reliably estimate the size of search trees explored by both optimization and decision procedures. We also demonstrate that these methods for estimating search tree size can be used to select the algorithm likely to perform best on a particular problem instance.
A Tool for Measuring Progress of Backtrack-Search Solvers
"... We introduce Satometer, a tool that can be used to estimate the percentage of the search space actually explored by a backtrack SAT solver. Satometer calculates a normalized minterm count for those portions of the search space identified by conflicts. The computation is carried out using a zero-supp ..."
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We introduce Satometer, a tool that can be used to estimate the percentage of the search space actually explored by a backtrack SAT solver. Satometer calculates a normalized minterm count for those portions of the search space identified by conflicts. The computation is carried out using a zero-suppressed BDD data structure and can have adjustable accuracy. The data provided by Satometer can help diagnose the performance of SAT solvers and can shed light on the nature of a SAT instance. 1.
