Constraint Satisfaction Problem

The security of a network configuration is based not just on the security of its individual components and their direct interconnections, but also on the potential for systems to interoperate indirectly across network routes. Such interoperation has been shown to provide the potential for circuitous paths across a network that violate security. In this paper we propose a constraint-based framework for representing access control configurations of systems. The secure reconfiguration of a system is depicted as a constraint satisfaction problem.

This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of outsourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a Constraint Satisfaction Problem (CSP). It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.

This paper presents a comparative study of Evolutionary Algorithms (EAs) for Constraint Satisfaction Problems (CSPs). We focus on EAs where fitness is based on penalization of constraint violations and the penalties are adapted during the execution. Three different EAs based on this approach are implemented. For highly connected constraint networks, the results provide further empirical support to the theoretical prediction of the phase transition in binary CSPs.

Project-driven manufacturing, based on the make-to-order or the build-to-order principle and predominant in small and medium-size enterprises (SMEs), calls for an efficient solution of large combinatorial problems, especially in such areas as task scheduling or resource management. This paper addresses the problem of finding a computationally effective approach to scheduling a new project subject to constraints imposed by a multi-project environment. A constraint programming (CP) modeling framework is discussed in the context of an efficient decomposition of the constraint satisfaction problem (CSP) and the evaluation of strategies for pruning the search tree. The proposed approach is illustrated through examples of its application to the evaluation of a new production order.

This paper focuses on Demand-Side load Management applied to residential sector. A home automation system controlling household energy is proposed. It is decomposed into three layers: anticipation, reactive and device layers. This paper deals with anticipation layer that allocates energy in taking into account predicted events. It consists in computing both the starting times of some services and to determine set points of others while satisfying the maximal power constraint. A Constraint Satisfaction Problem formulation has been proposed. Because the complexity is NP-Hard, a tabu search is used to solve the problem. It maximizes user comfort and minimizes energy cost. An application example is presented.

Conventional techniques for the constraint satisfaction problem (CSP) have had considerable success in their applications. However, there are many areas in which the performance of the basic approaches may be improved. These include heuristic ordering of certain tasks performed by the CSP solver, hybrids which combine compatible solution techniques and graph based methods which exploit the structure of the constraint graph representation of a CSP. Also, conventional constraint satisfaction techniques only address problems with hard constraints (i.e. each of which are completely satisfied or completely violated, and all of which must be satisfied by a valid solution). Many real applications require a more flexible approach which relaxes somewhat these rigid requirements. To address these issues various approaches have been developed. This paper attempts a systematic review of them.

An analysis of unified framework standing behind a methodology in object oriented decision support system design has been analysed. First of all the consistency of the assumed knowledge bases describing an object (enterprise) and requests (standard options supporting a decision maker), respectively are examined. Then the knowledge base representation is transformed into representation of so called constraint satisfaction problem (CSP). Possible ways of the CSP decomposition as well as possibility of different programming languages application lead to a problem of searching for a distribution strategy which gives a possibility to interact in an on-line mode.

This paper studies the problems involved in solving, rating and generating Sudoku puzzles with genetic algorithms (GA). Sudoku is a number puzzle that has recently become a worldwide phenomenon. Sudoku can be regarded as a constraint satisfaction problem. When solved with genetic algorithms it can be handled as a multi-objective optimization problem. The three objectives of this study was: 1) to test if genetic algorithm optimization is an efficient method for solving Sudoku puzzles, 2) can GA be used to generate new puzzles efficiently, and 3) can GA be used as a rating machine that evaluates the difficulty of a given Sudoku puzzle. The last of these objectives is approached by testing if puzzles that are considered difficult for a human solver are also difficult for the genetic algorithm. The results presented in this paper seem to support the conclusion that these objectives are reasonably well met with genetic algorithm optimization.

With 6-man Chess essentially solved, the available 6-man Endgame Tables (EGTs) have been scanned for zugzwang positions where, unusually, having the move is a disadvantage. Review statistics together with some highlights and positions are provided here: the complete information is available on the ICGA website. An outcome of the review is the observation that the definition of zugzwang should be revisited, if only because the presence of en passant capture moves gives rise to three new, asymmetric types of zugzwang.

Because many real-world problems can be represented and solved as constraint satisfaction problems, the development of effective, efficient constraint solvers is important. A solver's success depends greatly upon the heuristics chosen to guide the process; some heuristics perform well on one class of problems, but are less successful on another. ACE is a constraint solver that learns to customize a mixture of heuristics to solve a class of problems. The work described here accelerates that learning by setting higher performance standards. ACE now recognizes when its current learning attempt is not promising, abandons the responsible training problems, and restarts the entire learning process. This paper describes how such full restart (of the learning process rather than of an individual problem) demands careful evaluation if it is to provide effective learning and robust testing performance.

This paper explains why learning to solve constraint problems is so difficult, and describes a set of methods that has been effective on a broad variety of problem classes. The primary focus is on learning an effective search algorithm as a weighted subset of ordering heuristics. Experiments show the impact of several novel techniques on a variety of problems. When a planning problem is cast as a constraint satisfaction problem (CSP), it can use the representational expressiveness and inference power inherent in constraint programming (Nareyek et al. 2005). If such an encoding lacks the necessary planning knowledge, a program might learn effective solution methods. Our thesis is that it is possible to learn to solve constraint satisfaction problems from experience. In this scenario, a program is given a set of CSPs and a set of search heuristics. It is then expected to learn an effective search algorithm, represented as a weighted combination of some subset of those heuristics. From...

In current model-driven development approaches, software models are the primary artifacts of the development process. Therefore, assessment of their correctness is a key issue to ensure the quality of the final application. Research on model consistency has focused mostly on the models' static aspects. Instead, this paper addresses the verification of their dynamic aspects, expressed as a set of operations defined by means of pre/postcondition contracts. This paper presents an automatic method based on Constraint Programming to verify UML models extended with OCL constraints and operation contracts. In our approach, both static and dynamic aspects are translated into a Constraint Satisfaction Problem. Then, compliance of the operations with respect to several correctness properties such as operation executability or determinism are formally verified.

In this paper the partial constraint satisfaction problem PCSP is introduced and formulated as a f0; 1g-programming problem. We de ne the partial constraint satisfaction polytope as the convex hull of feasible solutions for this programming problem. As examples of the class of problems studied we mention the frequency assignment problem and the maximum satis ability problem. Lifting theorems are presented and some classes of facet-de ning valid inequalities for PCSP are given. Computational results show that these valid inequalities reduce the gap between LP-value and IP-value substantially.

Many real-world problems can be represented and solved as constraint satisfaction problems, but their solution requires the development of effective, efficient constraint solvers. A constraint solver’s success depends greatly upon the heuristics chosen to guide search. Some heuristics perform well on one class of problems, but are less successful on others. The solver described here learns a weighted combination of heuristic recommendations customized for a given class of problems. A pre-existing algorithm has weights learned for heuristics from the number of nodes explored during learning. We present here a new algorithm which learns weights for heuristics that consider the nuances of relative support for actions. The resultant performance is statistically significantly better than that of traditional individual heuristics.

Many data-driven social and Web applications involve collaboration and coordination. The vision of declarative data-driven coordination (D3C), proposed in [9], is to support coordination in the spirit of data management: to make it data-centric and to specify it using convenient declarative languages. This paper introduces entangled queries, a language that extends SQL by constraints that allow for the coordinated choice of result tuples across queries originating from different users or applications. It is nontrivial to define a declarative coordination formalism without arriving at the general (NP-complete) Constraint Satisfaction Problem from AI. In this paper, we propose an efficiently enforcible syntactic safety condition that we argue is at the sweet spot where interesting declarative power meets applicability in large scale data management systems and applications. The key computational problem of D3C is to match entangled queries to achieve coordination. We present an efficient matching algorithm which statically analyzes query workloads and merges coordinating entangled queries into compound SQL queries. These can be sent to a standard database system and return only coordinated results. We present the overall architecture of an implemented system that contains our evaluation algorithm; we also evaluate the performance of the matching algorithm experimentally on realistic coordination workloads.

Swarm . Optimizer which deals with permutation problems. Particles are defined as permutations of a group of unique values. Velocity updates are redefined based on the similarity of two particles. Particles change their permutations with a random rate defined by their velocities. A mutation factor is introduced to prevent the current pBest from becoming stuck at local minima. Preliminary study on the n-queens problem shows that the modified PSO is promising in solving constraint sstisfication problems.

Problem solvers, both human and machine, have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the problem class, and their mutual interactions may not be well understood. The long-term goal of our work is to learn how to select appropriately from among a large body of heuristics, and how to combine them into a mixture that works well on a specific class of problems. The principal result reported here is that randomly chosen subsets of heuristics can improve the identification of an appropriate mixture of heuristics. A self-supervised learner uses this method here to learn to solve constraint satisfaction problems quickly and effectively.

In this paper we present a new approach to modeling finite set domain constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We show that it is possible to construct an efficient set domain propagator which compactly represents many set domains and set constraints using ROBDDs. We demonstrate that the ROBDD-based approach provides unprecedented flexibility in modeling constraint satisfaction problems, leading to performance improvements. We also show that the ROBDD-based modeling approach can be extended to the modeling of integer and multiset constraint problems in a straightforward manner. Since domain propagation is not always practical, we also show how to incorporate less strict consistency notions into the ROBDD framework, such as set bounds, cardinality bounds and lexicographic bounds consistency. Finally, we present experimental results that demonstrate the ROBDD-based solver performs better than various more conventional constraint solvers on several standard set constraint problems.

In classical CLP(FD) systems, domains of variables are completely known at the beginning of the constraint propagation process. However, in systems interacting with an external environment, acquiring the whole domains of variables before the beginning of constraint propagation may cause waste of computation time, or even obsolescence of the acquired data at the time of use.

We define a quantitative constraint language subsuming two calculi well-known in QSR 4 : Frank's cone-shaped and projection-based calculi of cardinal direction relations. The language is based on convex constraints of the form (α, β)(x, y), with α, β ∈ [0, 2π) and (β−α) ∈ [0, π): the meaning of such a constraint is that point x belongs to the (convex) cone-shaped area rooted at y, and bounded by angles α and β. The general form of a constraint is a disjunction of the form [(α1, β1) ∨ · · · ∨ (αn, βn)](x, y), with (αi, βi)(x, y), i = 1 . . . n, being a convex constraint as described above: the meaning of such a general constraint is that, for some i = 1 . . . n, (αi, βi)(x, y) holds. A conjunction of such general constraints is a TCSP-like CSP, which we will refer to as an SCSP (Spatial Constraint Satisfaction Problem). We describe how to compute converse, intersection and composition of SCSP constraints, allowing thus to achieve path consistency for an SCSP. We show how to translate a convex constraint into a conjunction of linear inequalities on variables consisting of the arguments' coordinates. Our approach to effectively solving a general SCSP is then to adopt a solution search algorithm using (1) path consistency as the filtering method during the search, and (2) the Simplex algorithm, guaranteeing completeness, at the leaves of the search tree.

The clique problem consists in determining whether an undirected graph G of order n contains a clique of order . In this paper we are concerned with the decremental version of clique problem, where the property of containing an -clique is dynamically checked during deletions of nodes. We provide an improved dynamic algorithm for this problem for every fixed value of ≥ 3. Our algorithm naturally applies to filtering for the constraint satisfaction problem. In particular, we show how to speed up the filtering based on an important local consistency property: the inverse consistency.

Constraint satisfaction problem is considered as an important area of research in artificial intelligence, so that many problems in artificial intelligence and other fields of computer science can be defined as constraint satisfaction problems (CSPs). On the other hand, there are situations where a problem needs to be solved in a distributed mode, such that it is impossible to use a central controller. In these conditions, the importance of multiagent systems is specified.

In this paper, we introduce a new solving algorithm for Constraint Satisfaction Problems (CSP). It performs an overall local search helped with a domain filtering technique to prune the search space. Conflicts detected during filtering are used to guide the search. First experiments with a tabu version of the algorithm have shown good results on hard instances of open shop scheduling problems. It competes well with the best highly specialized algorithms.

Call control features (e.g., call-divert, voice-mail) are primitive options to which users can subscribe off-line to personalise their service. The configuration of a feature subscription involves choosing and sequencing features from a catalogue and is subject to constraints that prevent undesirable feature interactions at run-time. When the subscription requested by a user is inconsistent, one problem is to find an optimal relaxation. In this paper, we show that this problem is NP-hard and we present a constraint programming formulation using the variable weighted constraint satisfaction problem framework. We also present simple formulations using partial weighted maximum satisfiability and integer linear programming. We experimentally compare our formulations of the different approaches; the results suggest that our constraint programming approach is the best of the three overall.

Optimizating semantic web service compositions is known to be NP-hard, so most approaches restrict the number of services and offer poor scalability. We address the scalability issue by selecting compositions which satisfy a set of constraints rather than attempting to produce an optimal composition. Firstly, we define constraints within an innovative and extensible quality model designed to balance semantic fit (or functional quality) with quality of service (QoS) metrics. The semantic fit criterion evaluates the quality of semantic links between the semantic description of Web services parameters, whilst QoS focuses on nonfunctional criteria of services. Coupling these criteria allows us to further constrain and select valid compositions. To allow the use of this model in the context of millions of services as foreseen by the strategic EC-funded project SOA4All, we i) formulate the selection problem as a Constraint Satisfaction Problem and ii) test the use of a stochastic search method. Finally we compare the latter with stateof-the-art approaches.

Configuration is the task of composing product models of complex systems from parameterisable components. This task demands for powerful knowledge-representation formalisms to capture the great variety and complexity of configurable product models. Furthermore, efficient reasoning methods are required to provide intelligent interactive behavior in configurator software, such as solution search, satisfaction of user preferences, personalization, optimization, diagnosis, etc.

Probing technology has been used as a fault detection and identification method in computer networks and successful applications have been reported. One of the most appealing features of probing-based schemes is that it is an active approach. A set of probes can be sent on a periodic basis. If a network failure is detected, the outcomes of these probes are further analyzed to determine the root cause of the problem. However, the availability of a large set of such probes may in fact place a huge burden on management systems in terms of extra management traffic and storage space. Hence, the need of minimizing such a probing set has become highly desirable. In this work, we propose a preplanned probe selection scheme, in which a small set of probes are chosen such that it maintains the diagnostic power of the original set. The new approach is based on the constraint satisfaction problem paradigm and its powerful search techniques are exploited. The efficiency of the new algorithm has been demonstrated by the results reported.

In classical CLP(FD) systems, domains of variables are completely known at the beginning of the constraint propagation process. However, in systems interacting with an external environment, acquiring the whole domains of variables before the beginning of constraint propagation may cause waste of computation time, or even obsolescence of the acquired data at the time of use.

Constraint satisfaction problems, where values are sought for problem variables subject to restrictions on which combinations of values are acceptable, have many applications in artificial intelligence. Conventional learning methods acquire individual tuples of inconsistent values. These learning experiences can be generalized. We propose a model of generalized learning, based on inconsistency preserving mappings, which is sufficiently focused so as to be computationally cost effective. Rather than recording an individual inconsistency that led to a failure, and looking for that specific inconsistency to recur, we observe the context of a failure, and then look for a related context in which to apply our experience opportunistically. As a result we leverage our learning power. This model is implemented, extended and evaluated using two simple but important classes of constraint problems.

We give an approximate and often extremely fast method of building a particular kind of portfolio in finance, here called a portfolio design (PD), with applications in the credit derivatives market, for example when designing collateralised debt obligations squared (CDO 2) transactions. A PD generalises a balanced incomplete block design (BIBD) and is usually harder to build. Worse, typical financial PDs are an order of magnitude larger than the largest BIBDs built so far by constraint programs, and in practice an optimisation version of the problem of building PDs has to be solved. Our method is based on embedding small designs, whose determination is itself a constraint satisfaction problem, into the original large design. Together with the detection of when a PD might be a BIBD, symmetry breaking, extended reuse of previously built PDs, and admissibility checking during search, the performance of the method becomes good enough for designing (near-)optimal CDO 2 transactions, with sizes common in the credit derivatives market, within minutes. For example, we optimally build a typical financial PD, which has over 10 746 symmetries, in just a few minutes. The high quality of our approximate designs can be assessed by comparison with a lower bound on the optimum. Our designs sufficiently improve the currently best ones so as often to make the difference