Automated Symmetry Breaking and Model Selection in Conjure

Self Cite

Abstract. Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously, effective streamlined models have been constructed by hand, requiring an expert to examine closely solutions to small instances of a problem class and identify regularities. We present a system that automatically generates many conjectured regularities for a given Essence specification of a problem class by examining the domains of decision variables present in the problem specification. These conjectures are evaluated independently and in conjunction with one another on a set of instances from the specified class via an automated modelling tool-chain comprising of Conjure, Savile Row and Minion. Once the system has identified effective conjectures they are used to generate streamlined models that allow instances of much larger scale to be solved. Our results demonstrate good models can be identified for problems in combinatorial design, Ramsey theory, graph theory and group theory -often resulting in order of magnitude speed-ups.

Section: Exploiting Essence Domain Annotationsmentioning

confidence: 99%

Automatically Generating Streamlined Constraint Models with Essence and Conjure

Wetter

Akgün

Miguel

2015

Self Cite

“…Our approach to model selection follows that we reported in [1], but with an improved method of producing a set of winning models described below. It takes as input a set of instances drawn from the target problem class.…”

Section: Racing For Automated Model Selectionmentioning

confidence: 99%

“…We obtain a single model for E * automatically using CONJURE and the Compact heuristic [1] -this is sufficient because the enumeration problem is typically easy. MINION is then used to find all solutions to the model.…”

Section: Uniform Versus Non-uniform Samplingmentioning

confidence: 99%

“…In our previous work [1] we developed a racing process to select among these candidate models, in which a set of training instances drawn from the problem class being modelled is used to gauge relative model performance. For this process to be effective, it is important that the instances chosen are discriminating: if all models solve a given instance in a trivial amount of time, or if no model solves it in the time available, then the instance is not useful for model selection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discriminating Instance Generation for Automated Constraint Model Selection

Gent

Hussain

Jefferson

et al. 2014

Self Cite

One approach to automated constraint modelling is to generate, and then select from, a set of candidate models. This method is used by the automated modelling system CONJURE. To select a preferred model or set of models for a problem class from the candidates CONJURE produces, we use a set of training instances drawn from the target class. It is important that the training instances are discriminating. If all models solve a given instance in a trivial amount of time, or if no models solve it in the time available, then the instance is not useful for model selection. This paper addresses the task of generating small sets of discriminating training instances automatically. The instance space is determined by the parameters of the associated problem class. We develop a number of methods of finding parameter configurations that give discriminating training instances, some of them leveraging existing parameter-tuning techniques. Our experimental results confirm the success of our approach in reducing a large set of input models to a small set that we can expect to perform well for the given problem class.

“…Using training instances drawn from the problem class under consideration, streamliner candidates are evaluated via a toolchain consisting of the automated constraint modelling tools Conjure [1][2][3][4] and Savile Row [30][31][32], and the constraint solver Minion [20]. Promising candidates, which retain at least one solution to the training instances while significantly reducing search, are used to solve more difficult instances from the same problem class.…”

mentioning

confidence: 99%

Automatic Generation and Selection of Streamlined Constraint Models via Monte Carlo Search on a Model Lattice

Spracklen

Akgün

Miguel

2018

Self Cite

Abstract. Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously it has been established that it is possible to generate streamliners automatically from abstract constraint specifications in Essence and that effective combinations of streamliners can allow instances of much larger scale to be solved. A shortcoming of the previous approach was the crude exploration of the power set of all combinations using depth and breadth first search. We present a new approach based on Monte Carlo search over the lattice of streamlined models, which efficiently identifies effective streamliner combinations.