Reasoning from last conflict(s) in constraint programming

Lecoutre, Christophe; Saïs, Lakhdar; Tabary, Sébastien; Vidal, Vincent

doi:10.1016/j.artint.2009.09.002

Cited by 40 publications

(31 citation statements)

References 24 publications

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“…Sometimes, only a few random choices are made and the same search tree is likely to be explored from one restart to another. We apply a simple nogood recording technique similar to Lecoutre et al (2007) to compensate for this drawback.…”

Section: Nogood Recording From Restartsmentioning

confidence: 99%

An Optimal Constraint Programming Approach to the Open-Shop Problem

Malapert

Cambazard

Guéret

et al. 2012

INFORMS Journal on Computing

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This paper presents an optimal constraint programming approach for the Open-Shop scheduling problem, which integrates recent constraint propagation and branching techniques with new upper bound heuristics. Randomized restart policies combined with nogood recording allow to search diversification and learning from restarts. This approach is compared with the best-known metaheuristics and exact algorithms, and shows better results on a wide range of benchmark instances.

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Section: Nogood Recording From Restartsmentioning

confidence: 99%

An Optimal Constraint Programming Approach to the Open-Shop Problem

Malapert

Cambazard

Guéret

et al. 2012

INFORMS Journal on Computing

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“…Reification : Every constraint in Minion can be reified or reifyimplied [14] (also called half reification [6]): these combine a constraint C and a Boolean b into the new constraint b ⇐⇒ C and b =⇒ C respectively. Search Orders: Minion provides ten variable orderings, including static orderings, smallest domain first, conflict ordering [18] and weighted degree [2]. Global Propagation Level: Minion can perform higher levels of consistency, including Singleton Arc Consistency [5].…”

Section: The Constraint Solver Minionmentioning

confidence: 99%

Metamorphic Testing of Constraint Solvers

Akgün

Gent

Jefferson

et al. 2018

Lecture Notes in Computer Science

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Abstract. Constraint solvers are complex pieces of software and are notoriously difficult to debug. In large part this is due to the difficulty of pinpointing the source of an error in the vast searches these solvers perform, since the effect of an error may only come to light long after the error is made. In addition, an error does not necessarily lead to the wrong result, further complicating the debugging process. A major source of errors in a constraint solver is the complex constraint propagation algorithms that provide the inference that controls and directs the search. In this paper we show that metamorphic testing is a principled way to test constraint solvers by comparing two different implementations of the same constraint. Specifically, specialised propagators for the constraint are tested against the general purpose table constraint propagator. We report on metamorphic testing of the constraint solver Minion. We demonstrate that the metamorphic testing method is very effective for finding artificial bugs introduced by random code mutation.

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“…Last Conflicts (LC) [9] has been shown to be highly profitable to complete search algorithms, both in constraint satisfaction and in automated artificial intelligence planning. The principle behind LC is to select in priority the last conflicting variables as long as they cannot be instantiated without leading to a failure.…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, Last Conflict (LC) [9] is a search mechanism that can be applied on top of any variable ordering heuristic. Precisely, the generalized form LC(k) works by recording and assigning first the k variables involved in the k last decisions that provoked a failure after propagation.…”

Section: Introductionmentioning

confidence: 99%

Conflict Ordering Search for Scheduling Problems

Hartert

Lecoutre

Schaus

2015

Lecture Notes in Computer Science

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Abstract. We introduce a new generic scheme to guide backtrack search, called Conflict Ordering Search (COS), that reorders variables on the basis of conflicts that happen during search. Similarly to generalized Last Conflict (LC), our approach remembers the last variables on which search decisions failed. Importantly, the initial ordering behind COS is given by a specified variable ordering heuristic, but contrary to LC, once consumed, this first ordering is forgotten, which makes COS conflict-driven. Our preliminary experiments show that COS -although simple to implement and parameter-free -is competitive with specialized searches on scheduling problems. We also show that our approach fits well within a restart framework, and can be enhanced with a value ordering heuristic that selects in priority the last assigned values.

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Reasoning from last conflict(s) in constraint programming

Cited by 40 publications

References 24 publications

An Optimal Constraint Programming Approach to the Open-Shop Problem

An Optimal Constraint Programming Approach to the Open-Shop Problem

Metamorphic Testing of Constraint Solvers

Conflict Ordering Search for Scheduling Problems

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