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Abscon 112 Toward more Robustness
"... Abstract. This paper describes the three main improvements made to the solver Abscon 109 [9]. The new version, Abscon 112, is able to automatically break some variable symmetries, infer allDifferent constraints from cliques of variables that are pairwise irreflexive, and use an optimized version of ..."
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Abstract. This paper describes the three main improvements made to the solver Abscon 109 [9]. The new version, Abscon 112, is able to automatically break some variable symmetries, infer allDifferent constraints from cliques of variables that are pairwise irreflexive, and use an optimized version of the STR (Simple Tabular Reduction) technique initially introduced by J. Ullmann for table constraints. 1 From Local to Global Variable Symmetries In [10], we have proposed to automatically detect variable symmetries of CSP instances by computing for each constraint scope a partition exhibiting locally symmetrical variables. From this local information that can be obtained in polynomial time, we can build a socalled lsvgraph whose automorphisms correspond to (global) variable symmetries. Interestingly enough, our approach allows us to disregard the representation (extension, intension, global) of constraints. Besides, the size of the lsvgraph is linear wrt the number of constraints (and their arity). To break symmetries from the generators returned by a graph automorphism
Efficient Algorithms for Singleton Arc Consistency
"... In this paper, we propose two original and efficient approaches for enforcing singleton arc consistency. In the first one, the data structures used to enforce arc consistency are shared between all subproblems where a domain is reduced to a singleton. This new algorithm is not optimal but it require ..."
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In this paper, we propose two original and efficient approaches for enforcing singleton arc consistency. In the first one, the data structures used to enforce arc consistency are shared between all subproblems where a domain is reduced to a singleton. This new algorithm is not optimal but it requires far less space and is often more efficient in practice than the optimal algorithm SACOpt. In the second approach, we perform several runs of a greedy search (where at each step, arc consistency is maintained), possibly detecting the singleton arc consistency of several values in one run. It is an original illustration of applying inference (i.e., establishing singleton arc consistency) by search. Using a greedy search allows benefiting from the incrementality of arc consistency, learning relevant information from conflicts and, potentially finding solution(s) during the inference process. We present extensive experiments that show the benefit of our two approaches.
SecondOrder Consistencies
"... In this paper, we propose a comprehensive study of secondorder consistencies (i.e., consistencies identifying inconsistent pairs of values) for constraint satisfaction. We build a full picture of the relationships existing between four basic secondorder consistencies, namely path consistency (PC), ..."
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In this paper, we propose a comprehensive study of secondorder consistencies (i.e., consistencies identifying inconsistent pairs of values) for constraint satisfaction. We build a full picture of the relationships existing between four basic secondorder consistencies, namely path consistency (PC), 3consistency (3C), dual consistency (DC) and 2singleton arc consistency (2SAC), as well as their conservative and strong variants. Interestingly, dual consistency is an original property that can be established by using the outcome of the enforcement of generalized arc consistency (GAC), which makes it rather easy to obtain since constraint solvers typically maintain GAC during search. On binary constraint networks, DC is equivalent to PC, but its restriction to existing constraints, called conservative dual consistency (CDC), is strictly stronger than traditional conservative consistencies derived from path consistency, namely partial path consistency (PPC) and conservative path consistency (CPC). After introducing a general algorithm to enforce strong (C)DC, we present the results of an experimentation over a wide range of benchmarks that demonstrate the interest of (conservative) dual consistency. In particular, we show that enforcing (C)DC before search clearly improves the performance of MAC (the algorithm that maintains GAC during search) on several binary and nonbinary structured problems. 1.
Encoding Table Constraints in CLP(FD) Based on Pairwise AC
"... Abstract. We present an implementation of table constraints in CLP(FD). For binary constraints, the supports of each value are represented as a finitedomain variable, and action rules are used to propagate value exclusions. The bitvector representation of finite domains facilitates constanttime r ..."
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Abstract. We present an implementation of table constraints in CLP(FD). For binary constraints, the supports of each value are represented as a finitedomain variable, and action rules are used to propagate value exclusions. The bitvector representation of finite domains facilitates constanttime removal of unsupported values. For nary constraints, we propose pairwise arc consistency (AC), which ensures that each value has a support in the domain of every related variable. Pairwise AC does not require introducing new problem variables as done in binarization methods and allows for compact representation of constraints. Nevertheless, pairwise AC is weaker than general arc consistency (GAC) in terms of pruning power and requires a final check when a constraint becomes ground. To remedy this weakness, we propose adopting early checks when constraints are sufficiently instantiated. Our experimentation shows that pairwise AC with early checking is as effective as GAC for positive constraints. 1
Short and Long Supports for Constraint Propagation
"... Specialpurpose constraint propagation algorithms frequently make implicit use of short supports — by examining a subset of the variables, they can infer support (a justification that a variablevalue pair may still form part of an assignment that satisfies the constraint) for all other variables an ..."
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Specialpurpose constraint propagation algorithms frequently make implicit use of short supports — by examining a subset of the variables, they can infer support (a justification that a variablevalue pair may still form part of an assignment that satisfies the constraint) for all other variables and values and save substantial work – but short supports have not been studied in their own right. The two main contributions of this paper are the identification of short supports as important for constraint propagation, and the introduction of HaggisGAC, an efficient and effective general purpose propagation algorithm for exploiting short supports. Given the complexity of HaggisGAC, we present it as an optimised version of a simpler algorithm ShortGAC. Although experiments demonstrate the efficiency of ShortGAC compared with other generalpurpose propagation algorithms where a compact set of short supports is available, we show theoretically and experimentally that HaggisGAC is even better. We also find that HaggisGAC performs better than GACSchema on fulllength supports. We also introduce a variant algorithm HaggisGACStable, which is adapted to avoid work on backtracking and in some cases can be faster and have significant reductions in memory use. All the proposed algorithms are excellent for propagating disjunctions of constraints. In all experiments with disjunctions we found our algorithms to be faster than Constructive Or and GACSchema by at least an order of magnitude, and up to three orders of magnitude. 1.
Constraints manuscript No. (will be inserted by the editor) Promoting Robust Blackbox Solvers through Competitions ∗
"... Abstract This paper presents the experiences of the organisers of the four constraint solver competitions which were held in conjunction with CP in the previous years. The paper mainly focuses on the competitions which were held in 2008 and 2009, outlines the reasons for organising the competitions, ..."
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Abstract This paper presents the experiences of the organisers of the four constraint solver competitions which were held in conjunction with CP in the previous years. The paper mainly focuses on the competitions which were held in 2008 and 2009, outlines the reasons for organising the competitions, describes how the solvers were evaluated, and presents lessons, observations, and general trends. Keywords solver competition · experimental evaluation · robust constraint solvers · blackbox 1 Why Organise a Competition? This paper presents the experiences of the organisers of the four constraint solver competitions, all of which were held in conjunction with the international conferences on principles and practice of Constraint Programming (CP). The paper mainly focuses on the competitions organised in 2008 and 2009. In the remainder of this section, we outline three main motivations for organising a competition: solver comparison, problem instance exchange, and the promotion of robust constraint solvers.
An Optimal Filtering Algorithm for Table Constraints
"... Abstract. Filtering algorithms for table constraints are constraintbased, which means that the propagation queue only contains information on the constraints that must be reconsidered. This paper proposes four efficient valuebased algorithms for table constraints, meaning that the propagation queu ..."
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Abstract. Filtering algorithms for table constraints are constraintbased, which means that the propagation queue only contains information on the constraints that must be reconsidered. This paper proposes four efficient valuebased algorithms for table constraints, meaning that the propagation queue also contains information on the removed values. One of these algorithms (AC5TCTr) is proved to have an optimal time complexity of O(r.t + r.d) per table constraint. Experimental results show that, on structured instances, all our algorithms are two or three times faster than the state of the art STR2+ and MDD c algorithms. 1
Short and Long Supports Short and Long Supports for Constraint Propagation
"... Constraint solvers typically employ a systematic backtracking search, interleaving the choice of an instantiation of a decision variable with the propagation of the constraints to determine the consequences of the choice made. Specialpurpose constraint propagation algorithms (such as those for the ..."
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Constraint solvers typically employ a systematic backtracking search, interleaving the choice of an instantiation of a decision variable with the propagation of the constraints to determine the consequences of the choice made. Specialpurpose constraint propagation algorithms (such as those for the element constraint) frequently make implicit use of short supports — by examining a subset of the variables, they can infer support (a justification that a variable, value pair may still form part of an assignment that satisfies the constraint) for all other variables and values and save substantial work. However, to date general purpose propagation algorithms rely upon supports involving all variables. We demonstrate how to employ short supports in a new general purpose propagation algorithm called ShortGAC. Experiments demonstrate the efficiency of ShortGAC compared with other generalpurpose propagation algorithms. We identify inefficiencies in ShortGAC, and introduce a new algorithm HaggisGAC to address these. Experiments show HaggisGAC performing better on short supports than ShortGAC, and better than GACSchema on full length supports. Thus HaggisGAC is an excellent general purpose GAC algorithm for dealing with either short or long supports. We also show how it can be adapted to avoid work on backtracking. In some cases this can lead to significant reductions in memory use. To summarise, we have demonstrated the value of the explicit use of short supports, and introduced algorithms to exploit them, of which HaggisGAC seems to be the best. All the proposed algorithms are excellent for propagating disjunctions of constraints. In all experiments with disjunctions we found our algorithms to be faster than Constructive Or (Lagerkvist & Schulte, 2009) and GACSchema (Bessière & Régin, 1997) by at least an order of magnitude, and up to three orders of magnitude. Note to editors/reviewers Most of sections 26 of this paper are based on our IJ
Maintaining List Pointers During Search An Optimality Result on Maintaining List Pointers During Backtracking Search
"... I prove that a widely used technique for scanning lists in backtracking search algorithms has excellent optimality properties. The result applies to a simple general framework, which I present. It applies to critical algorithms such as watched literal unit propagation in SAT and important algorithms ..."
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I prove that a widely used technique for scanning lists in backtracking search algorithms has excellent optimality properties. The result applies to a simple general framework, which I present. It applies to critical algorithms such as watched literal unit propagation in SAT and important algorithms for maintaining generalised arc consistency in constraint satisfaction. I now show that both these techniques have optimal run time per branch in bigO terms when amortized across a search tree. Moreover the constant factor overhead of the worst case is only 2 in the simplest case. It is widely known that these methods are highly space efficient and effective in practice. My results help to explain this from a theoretical point of view. 1.
Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence Extending Simple Tabular Reduction with Short Supports
"... Constraint propagation is one of the key techniques in constraint programming, and a large body of work has built up around it. Specialpurpose constraint propagation algorithms frequently make implicit use of short supports — by examining a subset of the variables, they can infer support (a justifi ..."
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Constraint propagation is one of the key techniques in constraint programming, and a large body of work has built up around it. Specialpurpose constraint propagation algorithms frequently make implicit use of short supports — by examining a subset of the variables, they can infer support (a justification that a variablevalue pair still forms part of a solution to the constraint) for all other variables and values and save substantial work. Recently short supports have been used in general purpose propagators, and (when the constraint is amenable to short supports) speed ups of more than three orders of magnitude have been demonstrated. In this paper we present SHORTSTR2, a development of the Simple Tabular Reduction algorithm STR2+. We show that SHORTSTR2 is complementary to the existing algorithms SHORTGAC and HAGGISGAC that exploit short supports, while being much simpler. When a constraint is amenable to short supports, the short support set can be exponentially smaller than the fulllength support set. Therefore SHORTSTR2 can efficiently propagate many constraints that STR2+ cannot even load into memory. We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from fulllength supports, to provide a superior dropin replacement for STR2+. 1