An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems

Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo

doi:10.9781/ijimai.2016.3712

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…To take advantage of all cuts, the next step would be to define a portfolio solver exploiting the different kinds of cuts, the goal being to outperform each individual cut generation strategy. Portfolio approaches combine different solvers to get a globally better one, and their efficiency was already shown in the CP field [1,2,9]. [3] 115 [3] 115 [3] 115 [1] 121 118 130 [4] 130 [11] 129 [6] 130 [7] 134 134 6 133 [13] 133 [5] 133 [8] 133 [0] 154 154 150 [11] 153 [10] 153 [10] 150 [13]…”

Section: Methodsmentioning

confidence: 99%

Decomposition and Cut Generation Strategies for Solving Multi-Robot Deployment Problems

Pacheco¹,

Pralet²,

Roussel³

2019

Lecture Notes in Computer Science

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In this paper, we consider a multi-robot deployment problem involving a set of robots which must realize observation tasks at different locations and navigate through a shared network of waypoints. To solve this problem, we develop a two-level approach which alternates between (a) quickly obtaining high-level schedules based on a coarse grain CP model which approximates navigation tasks as setup times between observations, and (b) generating more accurate schedules based on a fine grain CP model which takes into account all resource usage conflicts during traversals of the shared network. The low-level layer also contains an explanation module able to generate constraints holding on high-level decision variables. These constraints (or cuts) account for interferences found in the low-level solutions and which the high-level scheduler should take into account to minimize the makespan. The proposed variants of the cut generation strategy are incomplete, the aim being to obtain good quality solutions in a short time, and they differ in the way they allow to diversify search. Experiments show the efficiency of this approach and the complementarity of the cut generation schemes proposed.

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Section: Methodsmentioning

confidence: 99%

Decomposition and Cut Generation Strategies for Solving Multi-Robot Deployment Problems

Pacheco¹,

Pralet²,

Roussel³

2019

Lecture Notes in Computer Science

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show abstract

“…CPHydra, however, computes the schedule of solvers differently, and does not define any heuristic for scheduling the selected solvers. CPHydra won the 2008 International CSP Solver Competition, but subsequent investigations (Amadini et al, 2013(Amadini et al, , 2014(Amadini et al, , 2016a showed some weaknesses in scalability and runtime minimization.…”

Section: Related Workmentioning

confidence: 99%

sunny-as2: Enhancing SUNNY for Algorithm Selection

Liu¹,

Amadini

Gabbrielli

et al. 2021

jair

Self Cite

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SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as, a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as, called sunny-as2, was then submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work we present the technical advancements of sunny-as2, by detailing through several empirical evaluations and by providing new insights. Its current version, built on the top of the preliminary version submitted to OASC, is able to outperform sunny-as and other state-of-the-art AS methods, including those who did not attend the challenge.

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“…Loreggia et al [47] introduce an automated way for generating an informative set of features by training a neural network on images extracted from problem instances. An evaluation of the portfolio approaches for CSPs is presented by Amadini [7,3].…”

Section: Portfolio Csp Solvers Using Machine Learningmentioning

confidence: 99%

Wombit: A Portfolio Bit-Vector Solver Using Word-Level Propagation

2018

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We develop an idea originally proposed by Michel and Van Hentenryck of how to perform bit-vector constraint propagation on the word level. Most operations are propagated in constant time, assuming the bit-vector fits in a machine word. In contrast, bit-vector SMT solvers usually solve bitvector problems by (ultimately) bit-blasting, that is, mapping the resulting operations to conjunctive normal form clauses, and using SAT technology to solve them. Bit-blasting generates intermediate variables which can be an advantage, as these can be searched on and learnt about. As each approach has advantages, it makes sense to try to combine them. In this paper, we describe an approach to bit-vector solving using word-level propagation with learning. We have designed alternative word-level propagators to Michel and Van Hentenryck's, and evaluated different variants of the approach. We have also experimented with different approaches to learning and back-jumping in the solver. Based on the insights gained, we have built a portfolio solver, Wombit, which essentially extends the STP bit-vector solver. Using machine learning techniques, the solver makes a judicious up-front decision about whether to use word-level propagation or fall back on bit-blasting.

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An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems

Cited by 8 publications

References 46 publications

Decomposition and Cut Generation Strategies for Solving Multi-Robot Deployment Problems

Decomposition and Cut Generation Strategies for Solving Multi-Robot Deployment Problems

sunny-as2: Enhancing SUNNY for Algorithm Selection

Wombit: A Portfolio Bit-Vector Solver Using Word-Level Propagation

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