Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers

Geschwender, Daniel J.; Karakashian, Shant; Woodward, Robert J.; Choueiry, Berthe Y.; Scott, Stephen

doi:10.1609/aaai.v27i1.8532

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…2012) and solving difficult CSPs with higher levels of consistency (Karakashian, Woodward, and Choueiry 2013). In particular, we are interested in applying AllSol and PerTuple locally to the clusters of a tree decomposition (Geschwender et al 2013). The performance of the two algorithms vary widely in practice: Indeed, one algorithm may finish reasonably fast while the other fails to terminate in a given time threshold.…”

Section: Consistency Algorithms Consideredmentioning

confidence: 99%

Characterizing Performance of Consistency Algorithms by Algorithm Configuration of Random CSP Generators

Geschwender

Woodward

Choueiry

2015

AAAI

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In Constraint Processing, many algorithms for enforcing the same level of local consistency may exist. The performance of those algorithms varies widely. In order to understand what problem features lead to better performance of one algorithm over another, we utilize an algorithm configurator to tune the parameters of a random problem generator and maximize the performance difference of two consistency algorithms for enforcing constraint minimality. Our approach allowed us to generate instances that run 1000 times faster for one algorithm over the other.

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Section: Consistency Algorithms Consideredmentioning

confidence: 99%

Characterizing Performance of Consistency Algorithms by Algorithm Configuration of Random CSP Generators

Geschwender

Woodward

Choueiry

2015

AAAI

Self Cite

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Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector

Blet

Ndiaye

Solnon

2014

Lecture Notes in Computer Science

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We consider a generic binary CSP solver parameterized by high-level design choices, i.e., backtracking mechanisms, constraint propagation levels, and variable ordering heuristics. We experimentally compare 24 different configurations of this generic solver on a benchmark of around a thousand instances. This allows us to understand the complementarity of the different search mechanisms, with an emphasis on Backtracking with Tree Decomposition (BTD). Then, we use a per-instance algorithm selector to automatically select a good solver for each new instance to be solved. We introduce a new strategy for selecting the solvers of the portfolio, which aims at maximizing the number of instances for which the portfolio contains a good solver, independently from a time limit.

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Improving Relational Consistency Algorithms Using Dynamic Relation Partitioning

Schneider

Woodward

Choueiry

et al. 2014

Lecture Notes in Computer Science

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Relational consistency algorithms are instrumental for solving difficult instances of Constraint Satisfaction Problems (CSPs), often allowing backtrack-free search. In this paper, we improve an algorithm for enforcing relational consistency by exploiting the property that the constraints of the dual encoding of a CSP are piecewise functional. This property allows us to partition a CSP relation into blocks of equivalent tuples at varying levels of granularity. Our new algorithm dynamically exploits those partitions. Our experiments show a significant improvement of the processing time for enforcing relational consistency.

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Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers

Cited by 4 publications

References 5 publications

Characterizing Performance of Consistency Algorithms by Algorithm Configuration of Random CSP Generators

Characterizing Performance of Consistency Algorithms by Algorithm Configuration of Random CSP Generators

Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector

Improving Relational Consistency Algorithms Using Dynamic Relation Partitioning

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