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The essence of ESSENCE: A constraint language for specifying combinatorial problems
 In Proceedings of the 20th International Joint Conference on Artificial Intelligence
, 2005
"... Abstract. Essence is a new language for specifying combinatorial (decision or optimisation) problems at a high level of abstraction. The key feature enabling this abstraction is the provision of decision variables whose values can be combinatorial objects, such as tuples, sets, multisets, relations, ..."
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Cited by 64 (15 self)
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Abstract. Essence is a new language for specifying combinatorial (decision or optimisation) problems at a high level of abstraction. The key feature enabling this abstraction is the provision of decision variables whose values can be combinatorial objects, such as tuples, sets, multisets, relations, partitions and functions. Essence also allows these combinatorial objects to be nested to arbitrary depth, thus providing, for example, sets of partitions, sets of sets of partitions, and so forth. 1
The design of ESSENCE: a constraint language for specifying combinatorial problems
 In: Proceedings of IJCAI07
, 2007
"... ESSENCE is a new formal language for specifying combinatorial problems in a manner similar to natural rigorous specifications that use a mixture of natural language and discrete mathematics. ESSENCE provides a high level of abstraction, much of which is the consequence of the provision of decision v ..."
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Cited by 46 (2 self)
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ESSENCE is a new formal language for specifying combinatorial problems in a manner similar to natural rigorous specifications that use a mixture of natural language and discrete mathematics. ESSENCE provides a high level of abstraction, much of which is the consequence of the provision of decision variables whose values can be combinatorial objects, such as tuples, sets, multisets, relations, partitions and functions. ESSENCE also allows these combinatorial objects to be nested to arbitrary depth, thus providing, for example, sets of partitions, sets of sets of partitions, and so forth. Therefore, a problem that requires finding a complex combinatorial object can be directly specified by using a decision variable whose type is precisely that combinatorial object. 1
Propositional Satisfiability and Constraint Programming: a Comparative Survey
 ACM Computing Surveys
, 2006
"... Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively independent threads of research, crossfertilising occasionally. These two approaches to problem solving have a lot in common, as evidenced by similar ideas underlying the branch and prune algorithms ..."
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Cited by 38 (4 self)
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Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively independent threads of research, crossfertilising occasionally. These two approaches to problem solving have a lot in common, as evidenced by similar ideas underlying the branch and prune algorithms that are most successful at solving both kinds of problems. They also exhibit differences in the way they are used to state and solve problems, since SAT’s approach is in general a blackbox approach, while CP aims at being tunable and programmable. This survey overviews the two areas in a comparative way, emphasising the similarities and differences between the two and the points where we feel that one technology can benefit from ideas or experience acquired
The Design of the Zinc Modelling Language
, 2008
"... Zinc is a new modelling language developed as part of the G12 project. It has four important characteristics. First, Zinc allows specification of models using a natural mathematicallike notation. To do so it supports overloaded functions and predicates and automatic coercion and provides arithmetic ..."
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Cited by 38 (12 self)
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Zinc is a new modelling language developed as part of the G12 project. It has four important characteristics. First, Zinc allows specification of models using a natural mathematicallike notation. To do so it supports overloaded functions and predicates and automatic coercion and provides arithmetic, finite domain and set constraints. Second, while Zinc is a relatively simple and small language, it can be readily extended to different application areas by means of powerful language constructs such as userdefined predicates and functions and constrained types. Third, Zinc provides sophisticated type and instantiation checking which allows early detection of errors in models. Finally, perhaps the main novelty in Zinc is that it is designed to support a modelling methodology in which the same conceptual model can be automatically mapped into different design models, thus allowing modellers to easily "plug and play" with different solving techniques and so choose the most appropriate for that problem. We describe in detail the various language features of Zinc and the many tradeoffs we faced in its design.
Tailoring solverindependent constraint models: A case study with essence’ and minion
 In Proceedings of the 7th International Symposium on Abstraction, Reformulation and Approximation
, 2007
"... Abstract. In order to apply constraint programming to a particular domain, the problem must first be modelled as a constraint satisfaction problem. There are typically many alternative models of a given problem, and formulating an effective model requires a great deal of expertise. To reduce this bo ..."
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Cited by 31 (21 self)
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Abstract. In order to apply constraint programming to a particular domain, the problem must first be modelled as a constraint satisfaction problem. There are typically many alternative models of a given problem, and formulating an effective model requires a great deal of expertise. To reduce this bottleneck, the Essence language allows the specification of a problem abstractly, i.e. without making modelling decisions. This specification is refined automatically by the Conjure system to a solverindependent constraint modelling language Essence ′. However, there is still significant work involved in translating an Essence ′ model for use with a particular constraint solver. This paper discusses this ‘tailoring’ process with reference to the constraint solver Minion. 1
Global Constraint Catalogue: Past, Present and Future
, 2006
"... The catalogue of global constraints is reviewed, focusing on the graphbased description of global constraints. A number of possible enhancements are proposed as well as several research paths for the development of the area. ..."
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Cited by 26 (2 self)
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The catalogue of global constraints is reviewed, focusing on the graphbased description of global constraints. A number of possible enhancements are proposed as well as several research paths for the development of the area.
M.: From Zinc to design model
 In: Proceedings of PADL’07, SpringerVerlag
, 2007
"... Abstract. We describe a preliminary implementation of the highlevel modelling language Zinc. This language supports a modelling methodology in which the same Zinc model can be automatically mapped into different design models, thus allowing modellers to easily “plug and play” with different solvin ..."
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Cited by 13 (2 self)
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Abstract. We describe a preliminary implementation of the highlevel modelling language Zinc. This language supports a modelling methodology in which the same Zinc model can be automatically mapped into different design models, thus allowing modellers to easily “plug and play” with different solving techniques and so choose the most appropriate for that problem. Currently, mappings to three very different design models based on constraint programming (CP), mixed integer programming (MIP) and local search are provided. Zinc is the first modelling language that we know of that supports such solver and techniqueindependent modelling. It does this by using an intermediate language called Flattened Zinc, and rewrite rules for transforming the Flattened Zinc model into one that is tailored to a particular solving technique. 1
Transforming and refining abstract constraint specifications
 In Proceedings of the Sixth Symposium on Abstraction, Reformulation and Approximation, volume 3607 of Lecture Notes in Computer Science
, 2005
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Effective Compilation of Constraint Models
, 2010
"... Constraint Programming is a powerful technique for solving largescale combinatorial (optimisation) problems. However, it is often inaccessible to users without expert knowledge in the area, precluding the widespread use of Constraint Programming techniques. This thesis addresses this issue in thre ..."
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Cited by 11 (0 self)
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Constraint Programming is a powerful technique for solving largescale combinatorial (optimisation) problems. However, it is often inaccessible to users without expert knowledge in the area, precluding the widespread use of Constraint Programming techniques. This thesis addresses this issue in three main contributions. First, we propose a simple ‘modelandsolve ’ approach, consisting of a framework where the user formulates a solverindependent problem model, which is then automatically tailored to the input format of a selected constraint solver (a process similar to compiling a highlevel modelling language to machine code). The solver is then executed on the input, solver, and solutions (if they exist) are returned to the user. This allows the user to formulate constraint models without requiring any particular background knowledge of the respective solver and its solving technique. Furthermore, since the framework can target several solvers, the user can explore different types of solvers. Second, we extend the tailoring process with model optimisations that can compensate for a wide selection of poor modelling choices that novices (and experts) in Constraint Programming often make and hence result in redundancies. The elimination of these redundancies
Acquiring parameters of implied global constraints
 Proceedings CP
, 2005
"... Abstract. This paper presents a technique for learning parameterized implied constraints. They can be added to a model to improve the solving process. Experiments on impliedGcc constraints show the interest of our approach. 1 ..."
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Cited by 8 (0 self)
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Abstract. This paper presents a technique for learning parameterized implied constraints. They can be added to a model to improve the solving process. Experiments on impliedGcc constraints show the interest of our approach. 1