1 Introduction
In this paper, the focus is on unsatisfiable constraint networks. More precisely, a new approach for extracting minimal cores (or, s for Minimally Unsatisfiable Cores) of constraint networks is proposed. A is a minimal (w.r.t. ) set of constraints that cannot be satisfied all together. When causes of unsatisfiability must be understood and the network must be reengineered and relaxed to become satisfiable, extracting s can be a cornerstone issue since a provides one explanation for unsatisfiability in terms of a minimal set of incompatible constraints. Despite bad worstcase computational complexity results, various approaches for extracting one have been proposed that appear tractable for many instances [8, 2, 21, 20, 18, 19, 17, 15, 14].
A of a network can also be defined as an unsatisfiable subnetwork formed of transition constraints, which are constraints that would allow this subnetwork to become satisfiable if any of them were removed. Powerful approaches to extraction are founded on transition constraints, both in the csp [17, 14] and the sat [23, 12, 13, 33, 23, 28, 4] domains. In this last area, a recent approach [32, 5] focuses on the following intuition. An assignment of values to the variables that satisfies all constraints except one is called a transition assignment and the unsatisfied constraint is a transition constraint: additional transition constraints might be discovered by socalled model rotation, i.e., by examining other assignments differing from the transition assignment on the value of one variable, only.
In the paper, an approach both extending and enhancing this latter technique is proposed in the constraint network framework. The main idea is to use local search for exploring the neighborhood of transition assignments in an attempt to find out other transition constraints. The technique is put in work in a socalled dichotomy destructive strategy à la dc(wcore) [17] to extract one . Extensive computational experimentations show that this approach outperforms both the model rotation technique from [5] and the performance of stateoftheart extractors.
The paper is organized as follows. In the next section, basic concepts, definitions and notations are provided. Section 3 focuses on existing techniques for extraction, including dc(wcore)like ones, and then on model rotation. In section 4, a local search procedure for exhibiting additional transition constraints is presented and motivated, whereas section 5 describes the full algorithm for extraction. Section 6 describes our experimental investigations and results before some promising paths for further research are presented in the conclusion.
2 Definitions and Notations
Constraint networks are defined as follows.
Definition 1 (Constraint network)
A constraint network (cn) is a pair , where

is a finite set of variables s.t. each variable has an associated finite instantiation domain, denoted ;

is a finite set of constraints s.t. each constraint involves a subset of variables of , called scope and denoted . translates an associated relation that contains all the values for the variables of that satisfy .
A constraint network where the scope of all constraints is binary can be represented by a nonoriented graph where each variable is a node and each constraint is an edge.
Example 1
Let where each variable has the same domain and let be a set of constraints. The constraint network can be represented by the graph of Fig. 1(a).
Definition 2 (Assignment and solution)
An assignment of a constraint network is an assignment of values to all variables of . A solution to is any assignment that satisfies all constraints of .
A form of Constraint Satisfaction Problem (csp) consists in checking whether a constraint network admits at least one solution. This decision problem is an NPcomplete problem. If admits at least one solution then is called satisfiable else is called unsatisfiable. The constraint network of Example 1 is unsatisfiable. When a constraint network is unsatisfiable, it admits at least one Minimal (w.r.t. ) Unsatisfiable Core (in short, ).
Definition 3 (Core and )
Let be a constraint network.
is
an unsatisfiable core, in short a core, of iff

is an unsatisfiable constraint network, and

and .
is a Minimal Unsatisfiable Core () of iff

is a core of , and

there does not exist any proper core of : is satisfiable.
Example 1. (cont’d) is unsatisfiable and admits only one , illustrated in Fig. 1(b).
Whenever is unsatisfiable, exhibits at least one . In the worst case, there can be a number of different s that is exponential in the number of constraints of (actually it is in . Note that different s of a same network can share constraints. Accordingly, all s need not be extracted in a stepbystep relaxation process to make the network become satisfiable. Especially, such an iterative process where at each step one is extracted and relaxed so that it becomes satisfiable, at most s need to be extracted.
Example 2
Fig. 2 depicts an unsatisfiable constraint network with three s, namely , and . In this example, each variable is given the same domain .
Extracting one from an unsatisfiable constraint network is a heavy computational task in the worst case. Indeed, checking whether a constraint network is a is DPcomplete [27]. Despite the aforementioned bad worstcase computational complexity property, various families of approaches that run in acceptable time for many instances have been proposed. We describe representatives of some of the main ones in the next section.
3 Extraction
Most recent approaches for extracting one from an unsatisfiable constraint network start by computing one core (which does not need to be minimal) of . This step can be optional because an unsatisfiable constraint network is already a core. During this step some information can however be collected that will help guiding the further minimization step. In this paper, we focus on this minimization step and make use of the wcore core extractor introduced in [17] as a preprocessing step, that we briefly describe hereafter.
3.1 wcore as a preprocessing step
When the unsatisfiability of a constraint network is proved thanks to a filtering search algorithm, wcore [17] delivers a core of that is formed of all the constraints that have been involved in the proof of unsatisfiability, namely all the constraints that have been used during the search to remove by propagation at least one value from the domain of any variable. Such constraints are called active. Therefore, when is shown unsatisfiable, active constraints form a core since the other constraints do not actually take part to this proof of inconsistency. The approach from [17] iterates this process until no smaller set of active constraints is found. Consequently, at the end of this first step, constraints that are not active can be removed safely while keeping a remaining constraint network that is unsatisfiable.
Clearly, the resulting core can depend on the order according to which the partial assignments are investigated, which is guided by some branching heuristic.
wcore takes advantage of the powerful dom/wdeg heuristic [16] (see also variants in e.g. [14]), which consists in attaching to each constraint a counter initialized to and that is incremented each time the corresponding constraint is involved in a conflict. In this respect, dom/wdegselects the variable with the smallest ratio between the current domain size and a weighted degree, which is defined as the sum of the counters of the constraints in which the variable is involved. This heuristic allows to focus on constraints that appear difficult to satisfy. The goal is not only to attempt to ease the search for inconsistency but also to record some indication that these constraints are probably prone to belong to a . Accordingly, it is proposed in
[17] to weigh the constraints via the dom/deg heuristic and use the wcore approach as a preprocessing step for extraction to attempt to reduce the size of the core. Likewise, our approach reuses this weighing information in the subsequent steps of the algorithm to compute one .3.2 Minimization step
Once a core has been extracted from a constraint network, it must be minimized so that if forms one . To this end, it might be necessary to check whether a constraint belongs or not to the set of s included within a core, which is a task in [9]. In practice, this step is often based on the identification of forms of transition constraints.
Definition 4 (Transition constraint)
Let an unsatisfiable constraint network. is a transition constraint of iff there exists an assignment of such that is a model of .
Example 1 (cont’d) Consider again Fig. 1(a). is a transition constraint. Indeed is unsatisfiable and is a solution of .
The following property is straightforward and directly follows from the definition of transition constraints.
Property 1
If is a transition constraint of a core then belongs to any of .
Clearly, all s of a core do not necessarily share a nonempty intersection and a constraint network might thus have no transition constraints. Actually, the process of finding out transition constraints is performed with respect to some subparts of the network adopting e.g. either socalled destructive or constructive approaches [8, 2, 20, 18, 17, 14]. For example, the constructive approaches (as in [8]) successively insert constraints taken from the core into a set of constraints until this latter set becomes unsatisfiable. At the opposite, destructive approaches [2] successively remove constraints from the initial core until the current network becomes satisfiable. Constraints are ordered and each time a transition constraint is discovered, it is placed at the beginning of the core according to this order. All constraints are tested according to the inverse order. It is also possible to use a dichotomy strategy in order to find out transition constraints [17]. Variants and combinations of these techniques can be traced back to e.g. QuickXplain [19, 24] and the combined approach [14].
Clearly, the order according to which the constraints are tested is critical for the efficiency of each approach. This order can be set according to the weighs of constraints computed during the wcore step. In the rest of the paper, we focus on a dichotomy destructive approach, which will be presented in more detail later on. Before that, let us briefly present a method that has been recently proposed in the sat research community to find more than one transition constraint at each main iteration of a extraction algorithm (in the sat domain, a is called a mus for Minimal Unsatisfiable Subformula).
3.3 Recursive Model Rotation
The model rotation approach (mr) has been introduced in [31]. It is based on the transition assignment concept.
Definition 5 (Transition assignment)
Let be a core. An assignment of is a transition assignment of iff falsifies only one constraint in .
Property 2
Let be a core and . is a transition constraint of iff there exists an transition assignment of that falsifies .
The proof is straightforward since is a transition constraint of iff is unsatisfiable and there exists a solution of iff falsifies only the constraint of .
When a transition assignment is found, the model rotation approach explores assignments that differ from w.r.t. only one value. If this close assignment also falsifies only one constraint , then belongs to every , too.
In [5], it is proposed to recursively perform model rotation. This extended technique is called Recursive Model Rotation: it is summarized in a csp version in Algorithm 1. This algorithm always makes local changes to the value of one variable in the transition assignment in trying to find out another transition assignment exhibiting another constraint (lines 3–5), without a call to a constraint network solver. Contrary to the initial model rotation technique, the process is not stopped when a transition assignment does not deliver an additional transition constraint. Instead, model rotation is recursively performed with all transition assignments found (lines 6–8). See e.g., [4] [3] and [30] for more on the use of model rotation to extract muses.
5
5
5
5
4 Local Search for Transition Constraints
In the following, we introduce a new approach for computing one by means of exhibiting transition constraints that relies on stochastic local search (in short, sls) as a kernel procedure. Whenever sls reaches an assignment that falsifies exactly one constraint , is a transition constraint and belongs to the final . Obviously, such an assignment is a local minima for sls, which can then explore neighborhood assignments, including other possible transition assignments that would be discovered by recursive model rotation. Hence, we have investigated a generic approach based on sls that we call Local Search for Transition Constraints, in short lstc.
Local search in the sat and csp domains is usually implemented as a tool intended to search for a model. On the contrary, we make use of sls to explore the neighborhood of a transition assignment in search for additional transition constraints. Accordingly, some adaptations were made to the usual sls scheme. Although its escape strategy is close to the socalled breakout method [26], the end criterion was modified in order to allow SLS to focus and stress on parts of the search space that are expectedly very informative, as proposed in [11, 1]. More precisely, the counter of iterations remaining to be performed is increased in a significant way each time an additional transition constraint has been discovered, as SLS might have reached a promising part of the search space that has not been explored so far. On the contrary, when an already discovered transition constraint is found again, the counter is decreased by the number of times has already been considered, as a way to guide SLS outside expectedly wellexplored parts of the search space. Otherwise, the counter is decremented at each step and the procedure ends when becomes negative.
The objective function was itself modified to enforce the satisfaction of the constraints already identified as belonging to the that will be exhibited. More precisely, these latter constraints have their weighs increased in order to be satisfied first.
Algorithm 2 summarizes the approach. It takes as input an unsatisfiable constraint network , an assignment and the current set of constraints that have already been recognized as belonging to the that will be exhibited. Note that in most calls to Algorithm 2, the parameter is a subpart of the constraint network for which a must be found; will represent the current result of a dichotomy destructive strategy in the calling procedure.
10
10
10
10
10
10
10
10
10
Also, does not need to be a transition assignment. When is empty, it is randomly initialized, like in a classical sls procedure. Otherwise, is a transition assignment which is used as the starting point of the search. The algorithm returns after this set has been possibly extended by additional constraints also belonging to this . The local search is a standard basic randomwalk procedure [29] where the objective function has thus been modified in order to take a specific weigh on each constraint into account. Note that these weighs are specific to the call to lstc and thus different from the counters delivered by the preprocessing step, which are used by the dichotomy strategy.
A local minimum of a sls algorithm is a state where there does not exist any assignment that can be reached by a single move of the local search and that would decrease the sum of the weighs of the falsified constraints. Each time a local minimum is reached, the method tries to collect information (lines 6–13). Thus, before applying an escape criterion (line 14), when there is only one constraint of that is falsified by the current assignment (i.e., when the current assignment is a transition assignment), must appear in the final . Two subcases are thus as follows. When does not already belong to , is inserted within (line 9) and the value of is increased (line 10). When already belongs to , a penalty under the form of a negative number is applied to (line 12) and the weigh of is incremented (line 13). In this way, the more a transition constraint is considered, the greater is the penalty. When SLS is not reaching a local minimum, the value of one variable of is changed in such a way that the sum of the weighs of the falsified constraints decreases (line 15). In both latter cases, the value of the counter is decreased at each loop (line 16). Finally, when reaches a strictly negative value, a set of constraints included in the final to be exhibited is returned (line 17).
Let us stress that Algorithm 2 without colorized lines (6 to 13) is a mere standard stochastic local search procedure.
Noticeably, this approach differs from [10, 13] where a different form of sls was used to extract muses. First, [10, 13] was dedicated to the Boolean case using specific features of the clausal Boolean framework: extending it to the general constraint networks setting while still obtaining acceptable running times for many reallife instances remains an open challenge. Second, it was used as a fastpreprocessing step to locate an upperapproximation of a mus. The role of lstc is different: this procedure will be called during the finetuning process of the approximation delivered by the preprocessing. Finally, [10] and [13] were based on the socalled critical clause concept to explore the search space, which is not generalized and adopted here in the general framework of constraint networks.
5 Dichotomy Core Extraction with Local Search
2
Algorithm 3 summarizes an algorithm that computes one of an unsatisfiable constraint network , based on the dichotomy strategy and relying on the local search procedure to extract additional transition constraints. As a preprocessing step, wcore delivers in an unsatisfiable core of that is not guaranteed to be minimal (line 1). , the set of constraints that have already been recognized as belonging to the , is then initialized to the empty set (line 2). The lastly discovered transition assignment is initialized to the empty set (line 3). According to a dichotomy strategy is initialized with half the constraints of , themselves selected according to the scores collected during the wcore preprocessing step (line 4). A dichotomybased loop is run until becomes empty. While there remain constraints in that have not yet been either removed from the candidate or inserted in this , the subnetwork is solved and the solution is stored in (line 6). By convention, when is a model of , it is not empty. In this case and when at the same time the number of constraints belonging to is different from , no conclusion can be made with this set of constraints and is then refined according to the dichotomy strategy. When is empty, is unsatisfiable and the constraints of can be removed from while keeping the unsatisfiability of this latter set (line 9). Finally, when is not empty and contains only one constraint , is a transition constraint and will appear in the final result (line 10) while the transition assignment is recorded in .
Calls to lstc are performed at each iteration step when or , with the following parameters: the current constraint network , the set of constraints already identified as belonging to in construction and the complete interpretation . These calls are thus intended to find out additional transition constraints with respect to . Note that after the first main iterations in the loop of Algorithm 3 have allowed a first transition assignment to be found, all calls to lstc are made with the lastly discovered transition constraints as a parameter. Although there can thus exist several calls to lstc with the same transition constraint, it is important to note that evolves, forming another constraint network at each call. It is thus transmitted to the local search procedure together with an expectedly “good” interpretation to start with. When becomes an empty set, this means that all constraints of have been proved to belong to the , i.e., . Thus, at the end of the loop identified as forming one of is returned (line 14).
Importantly, Algorithm 3 is complete in the sense that it is delivers one for any unsatisfiable in finite time (but it is exponentialtime in worst case scenarios, like all complete algorithms to find out one ).
Let us stress that this algorithm differs from the dichotomy destructive strategy dc(wcore) in [17] according to the colorized lines (namely lines 3, 11 and 12), only.
6 Experimental Results
In order to assess and compare the actual efficiency of the localsearch approach with other methods, and in particular the modelrotation one, we have considered all the benchmarks from the last csp solvers competitions [6, 7],^{2}^{2}2The benchmarks are available at http://www.cril.univartois.fr/~lecoutre which include binary vs. nonbinary, random vs. reallife, satisfiable vs. unsatisfiable csp instances. Among these instances, only the 772 benchmarks that were proved unsatisfiable in less than 300 seconds using our own C++ rclCsp ^{3}^{3}3 The executable is available at http://www.cril.univartois.fr/~lagniez csp solver were considered. rclCsp implements MAC embedding AC3 and makes use of the variable ordering heuristic.
Three approaches to the extraction problem have been implemented with rclCsp as kernel and experimentally compared: (1) dc(wcore) [17], namely a dichotomy destructive strategy with wcore as a preprocessing step, without any form of model rotation or local search to find out additional transition constraints, (2) dc(wcore) + recmr and (3) dc(wcore)+lstc. For the latter approach, the counter was initialized to and the bonus was set to that value, too. Furthermore, [25] was used as localsearch escape criterion and the advanced data structures proposed in [22] have been implemented. All three versions have been run on a Quadcore Intel XEON X5550 with 32GB of memory under Linux Centos 5. Timeout has been set to 900 seconds.
In Fig. 3, a cactus plot compares the three approaches in terms of the number of instances for which a was extracted, indicating the spent CPU time on the axis. Several observations can be made. First, similarly to the sat setting for model rotation [5], recursive model rotation improves performance in the sense that dc(wcore)+recmr found a for 632 instances whereas dc(wcore) solved 609 instances, only. Then, local search in its turn improves recursive model rotation according to the same criterion: dc(wcore)+lstc solved 663 instances. In addition to solving 54 and 31 additional instances respectively, dc(wcore)+lstc appears to be more efficient in terms of CPU time and less demanding than the other competing approaches in terms of the number of calls to a csp solver for the more challenging instances.
In Fig. 4, pairwise comparisons between the approaches are provided. Each comparison between two methods is done in terms of CPU time (subfigures (a) (c) and (e)) and of the number of calls to the MAC solver (subfigures (b), (d) and (f)), successively. For each scatter, the axis (resp. axis) corresponds to the CPU time (resp. ) obtained by the method labelled on the same axis. Each dot (, ) thus gives the results for a given benchmark instance. Thus, dots above (resp. below) the diagonal represent instances for which the method labelled on the axis is better (resp. worse) than the method labelled on the axis. Points on the vertical (resp. horizontal) dashed line mean that the method labelled on the axis (resp. axis) did not solved the corresponding instance before timeout.
First, the figure 4(a) shows that dc(wcore)+recmr solves instances faster than dc(wcore). Then, Fig. 4(c) and 4(e) show that, most generally, dc(wcore)+lstc finds one faster than both dc(wcore) and dc(wcore)+recmr manage to do it, provided that the instance is difficult in the sense that extracting a requires more than 100 seconds. On easier instances, the additional computational cost of local search (and model rotation) has often a negative impact on the global computing time, but this latter one remains however very competitive. Fig. 4(b) (d) and (f) show the extent to which recursive model rotation and local search reduce the number of calls to a csp solver, and thus of calls to an NPcomplete oracle, in order to find out one . Clearly, dc(wcore)+lstc often outperforms the other approaches in that respect. The observation of these three figures suggests that local search allows more transition constraints to be discovered by considering assignments in the neighborhood of the transition assignments. This intuition is confirmed by the experimental results reported in Fig. 5. In this latter figure, we give the percentage of the total size of the that has been found by recmr and lstc, respectively. It shows that lstc detects more transition constraints than recmr. Moreover, it shows that for almost all instances, more than half of the constraints in the are found thanks to local search when dc(wcore)+lstc is under consideration. This ability explains much of the performance gains obtained on difficult instances. Actually, local search detects the totality of the for many instances.
Finally, Tab. 1 reports the detailed results of each approach on a typical panel of instances from the benchmarks. The first four columns provide the name, numbers of constraints and variables of the instance, the number of remaining constraints after the preprocessing step, successively. Then, for each method, the CPU time, the size of the extracted (the s discovered by the various methods can differ) and the number of csp calls to find it are listed. In addition, for dc(wcore)+recmr (resp. dc(wcore)+lstc), the number (“by rot.”) of constraints of the detected by model rotation (resp. local search (“by LS”)) is provided. TO means timeout and the best computing time for each instance is shaded in grey. For example, s of a same size (94 constraints) were found for the cc10102 instance using each of the three methods. lstcwas best performing in extracting a in 28.28 seconds (vs. 49.38 and 55.36 seconds for the other methods). Note that all constraints in that were discovered through the lstc procedure.
Instances  Prep.  dc(wcore)  dc(wcore)+recmr  dc(wcore)+lstc  
Name  time  size  solver  time  size  by  solver  time  size  by  solver  
(sec)  calls  (sec)  rot.  calls  (sec)  LS  calls  
radar82432  64  144  41  213.84  8  31  136.15  8  6  15  110.90  8  7  10 
radar92842  81  168  47  62.39  2  12  43.86  2  1  10  50.28  2  2  8 
qKnights50add  1235  55  1233  358.21  5  148  355.81  5  4  126  151.64  5  5  16 
qKnights50mul  1485  55  1484  335.55  5  102  352.27  5  4  96  172.16  5  5  16 
qKnights25mul  435  30  435  37.58  5  268  30.26  5  4  212  9.19  5  5  13 
qKnights25add  310  30  309  22.88  5  127  22.38  5  4  110  9.49  5  5  14 
qKnights20add  200  25  198  6.22  5  73  5.96  5  4  58  3.69  5  5  13 
bdd30  2713  21  1157  TO      TO        855.96  10  3  59 
bdd2  2713  21  1307  TO      TO        725.23  10  4  49 
bdd25  2713  21  1392  719.65  10  53  677.24  10  1  48  590.79  10  3  38 
bdd18  2713  21  994  TO      TO        824.97  10  6  46 
bdd6  2713  21  802  TO      TO        530.59  9  4  28 
bdd3  2713  21  1551  910.64  11  75  889.34  11  0  74  748.77  11  2  45 
ssa0432003  738  435  594  TO      TO        801.17  307  301  265 
graph2f25.xml  2245  400  1364  19.48  43  841  17.18  43  10  702  20.48  43  41  197 
qcp106710  822  100  511  TO      TO        15.69  93  93  13 
qcp106711  822  100  413  11.29  49  776  7.86  49  43  446  1.99  49  49  18 
ruler349a4  582  9  276  TO      802.14  36  26  171  364.23  36  36  12 
ruler177a4  196  7  97  2.05  29  196  1.78  29  16  119  8.49  29  29  23 
ruler258a4  350  8  152  24.19  28  182  21.69  28  21  76  20.08  28  28  9 
cc10102  2025  100  381  55.36  94  579  49.38  94  45  291  28.28  94  94  20 
cc15152  11025  225  902  275.42  92  572  229.33  92  47  272  173.37  92  92  30 
cc12122  4356  144  333  137.13  93  571  101.32  93  47  271  54.49  93  93  22 
cc20202  36100  400  1232  TO      755.35  92  43  297  563.57  92  92  27 
ehi8529714  4111  297  265  1.43  38  428  1.54  37  11  420  2.19  37  33  113 
ehi8529764  4113  297  182  1.39  38  427  1.06  37  13  319  1.86  35  28  105 
ehi8529798  4124  297  682  0.69  22  192  0.47  22  11  118  1.09  21  20  61 
sostaillard44  160  32  159  396.21  42  346  319.14  42  22  186  293.55  42  35  89 
sostaillard49  160  32  160  322.84  37  302  275.31  37  13  232  195.03  37  29  104 
sostaillard410  160  32  148  37.46  29  268  33.46  29  12  190  45.05  29  25  100 
sostaillard525  325  50  279  186.26  10  38  181.76  10  9  15  204.69  10  10  10 
BlackHole1  431  64  142  105.03  75  658  240.09  80  56  269  8.08  75  73  24 
BlackHole5  431  64  140  208.35  77  657  118.04  77  58  253  18.98  82  80  48 
BlackHole0  431  64  139  96.92  75  645  102.16  77  56  291  10.59  75  72  46 
BlackHole4  431  64  140  100.07  77  664  21.48  77  55  252  11.59  77  74  48 
BlackHole3  431  64  142  476.00  80  700  283.92  80  58  261  13.43  75  74  35 
The full table can be downloaded at http://www.cril.fr/~mazure/bin/fulltabLSMUCGLM.pdf
7 Perspectives and conclusion
Clearly, the local search scheme proposed in this paper improves the extraction of one by means of destructive strategies and opens many perspectives. Although dichotomy strategies, as explored in this paper, are known to be the most efficient ones, it could be interesting to graft this local search scheme to constructive or QuickXplainlike methods. Also, note that we have not tried to finetune the various parameters of this local search scheme. In this respect, it would be interesting to devise forms of dynamical settings for these parameters that better take the recorded information about the previous search steps into account, as explored in [14]. In the future, we plan to explore more advanced concepts that are related to transition constraints in the goal of better guiding the local search towards promising parts of the search space. Especially, socalled critical clauses [12] in the Boolean framework could be generalized in various ways in the full constraint networks setting. Exploring the possible ways according to which lstc could benefit from this is a promising path for further research.
Acknowledgements
This work has been partly supported by a grant from the Région Nord/PasdeCalais and by an EC FEDER grant.
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