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26
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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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
Local search and constraint programming for the postenrolment course timetabling problem
 In Proceedings of the conference on the
, 2008
"... Abstract. We present a variety of approaches for solving the post enrolmentbased course timetabling problem, which was proposed as Track 2 of the 2007 International Timetabling Competition. We approach the problem using local search and constraint programming techniques. We show how to take advant ..."
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Abstract. We present a variety of approaches for solving the post enrolmentbased course timetabling problem, which was proposed as Track 2 of the 2007 International Timetabling Competition. We approach the problem using local search and constraint programming techniques. We show how to take advantage of a listcolouring relaxation of the problem. Our local search approach won Track 2 of the 2007 competition. Our best constraint programming approach uses an original problem decomposition. Incorporating this into a large neighbourhood search scheme seems promising, and provides motivation for studying complete approaches in further detail. 1
Markov constraints: steerable generation of Markov sequences
, 2010
"... Markov chains are a well known tool to model temporal properties of many phenomena, from text structure to fluctuations in economics. Because they are easy to generate, Markovian sequences, i.e. temporal sequences having the Markov property, are also used for content generation applications such as ..."
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Markov chains are a well known tool to model temporal properties of many phenomena, from text structure to fluctuations in economics. Because they are easy to generate, Markovian sequences, i.e. temporal sequences having the Markov property, are also used for content generation applications such as text or music generation that imitate a given style. However, Markov sequences are traditionally generated using greedy, lefttoright algorithms. While this approach is computationally cheap, it is fundamentally unsuited for interactive control. This paper addresses the issue of generating steerable Markovian sequences. We target interactive applications such as games, in which users want to control, through simple input devices, the way the system generates a Markovian sequence, such as a text, a musical sequence or a drawing. To this aim, we propose to revisit Markov sequence generation as a branch and bound constraint satisfaction problem (CSP). We propose a CSP formulation of the basic Markovian hypothesis as elementary Markov Constraints (EMC). We propose algorithms that achieve domainconsistency for the propagators of EMCs, in an eventbased implementation of CSP. We show how EMCs can be combined to estimate the global Markovian probability of a whole sequence, and accommodate for different species of Markov generation such as fixed order, variableorder, or smoothing. Such a formulation, although more costly than traditional greedy generation algorithms, yields the immense advantage of being naturally steerable, since control specifications can be represented by arbitrary additional constraints, without any modification of the generation algorithm. We illustrate our approach on simple yet combinatorial chord sequence and melody generation problems and give some performance results.
Connections and Integration with SAT Solvers: A Survey and a Case Study in Computational Biology
"... Boolean constraints play a fundamental rôle in optimization and constraint satisfaction. The resolution of these constraints has been the subject of intense and successful work during the past decade, and SAT solvers have reached a spectacular maturity. This chapter gives a brief overview of the rel ..."
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Boolean constraints play a fundamental rôle in optimization and constraint satisfaction. The resolution of these constraints has been the subject of intense and successful work during the past decade, and SAT solvers have reached a spectacular maturity. This chapter gives a brief overview of the relevant literature on modern SAT solvers and on the recent efforts to better integrate Boolean reasoning with other constraint satisfaction techniques. As a case study that illustrates the use of SAT and CP we consider an application in computational biology: the task to build gene regulatory networks (GRNs). We report on experiments made on this problem with a combined SAT/CP approach.
An automaton Constraint for Local Search
, 2011
"... We explore the idea of using automata to implement new constraints for local search. This is already a successful approach in constraintbased global search. We show how to maintain the violations of a constraint and its variables via a deterministic finite automaton that describes a ground checker ..."
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We explore the idea of using automata to implement new constraints for local search. This is already a successful approach in constraintbased global search. We show how to maintain the violations of a constraint and its variables via a deterministic finite automaton that describes a ground checker for that constraint. We extend the approach to counter automata, which are often much more convenient than finite automata, if not more independent of the constraint instance. We establish the practicality of our approach on several reallife combinatorial problems.
Domain Views for Constraint Programming
"... Abstract. Views are a standard abstraction in constraint programming: They make it possible to implement a single version of each constraint, while avoiding to create new variables and constraints that would slow down propagation. Traditional constraintprogramming systems provide the concept of var ..."
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Abstract. Views are a standard abstraction in constraint programming: They make it possible to implement a single version of each constraint, while avoiding to create new variables and constraints that would slow down propagation. Traditional constraintprogramming systems provide the concept of variable views which implement a view of the type y = f(x) by delegating all (domain and constraint) operations on variable y to variable x. This paper proposes the alternative concept of domain views which only delegate domain operations. Domain views preserve the benefits of variable views but simplify the implementation of valuebased propagation. Domain views also support noninjective views compositionally, expanding the scope of views significantly. Experimental results demonstrate the practical benefits of domain views. 1
Generation of Implied Constraints for AutomatonInduced Decompositions
"... Abstract—Automata, possibly with counters, allow many constraints to be expressed in a simple and highlevel way. An automaton induces a decomposition into a conjunction of already implemented constraints. Generalised arc consistency is not generally maintained on decompositions induced by counter a ..."
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Abstract—Automata, possibly with counters, allow many constraints to be expressed in a simple and highlevel way. An automaton induces a decomposition into a conjunction of already implemented constraints. Generalised arc consistency is not generally maintained on decompositions induced by counter automata with more than one state or counter. To improve propagation of automatoninduced constraint decompositions, we use automated tools to derive loop invariants from the constraint checker corresponding to the given automaton. These loop invariants correspond to implied constraints, which can be added to the decomposition. We consider two global constraints and derive implied constraints to improve propagation even to the point of maintaining generalised arc consistency. Keywordsconstraint programming; implied constraints; global constraints; generalised arc consistency; invariants; automata I.
DOI: 10.1017/S000000000000000 Printed in the United Kingdom Constraint programming for air traffic management: A survey1 In memory of Pascal Brisset
"... Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimisation, from capacity balancing to conflict solving, using many different degrees of fr ..."
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Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimisation, from capacity balancing to conflict solving, using many different degrees of freedom, such as rerouting, flightlevel changes, or groundholding schemes. These problems are usually of a large dimension (there are 30, 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.
Constraint Acquisition∗
"... Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the nonexpert user ..."
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Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the nonexpert user in the modelling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a system that uses a concise representation of the learner’s version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated. 1
Modelling with Option Types in MiniZinc?
"... Abstract. Option types are a powerful abstraction that allows the concise modelling of combinatorial problems where some decisions are relevant only if other decisions are made. They have a wide variety of uses: for example in modelling optional tasks in scheduling, or exceptions to a usual rule. O ..."
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Abstract. Option types are a powerful abstraction that allows the concise modelling of combinatorial problems where some decisions are relevant only if other decisions are made. They have a wide variety of uses: for example in modelling optional tasks in scheduling, or exceptions to a usual rule. Option types represent objects which may or may not exist in the constraint problem being modelled, and can take an ordinary value or a special value> indicating they are absent. The key property of variables of option types is that if they take the value> then the constraints they appear in should act as if the variable was not in the original definition. In this paper, we explore the different ways that basic constraints can be extended to handle option types, and we show that extensions of global constraints to option types cover existing and common variants of these global constraints. We demonstrate how we have added option types to the constraint modelling language MINIZINC. Constraints over variables of option types can either be handled by transformation into regular variables without extending the requirements on underlying solvers, or they can be passed directly to solvers that support them natively. 1