Showing papers on "Constraint satisfaction published in 2010"

Ensemble of Constraint Handling Techniques

Abstract: During the last three decades, several constraint handling techniques have been developed to be used with evolutionary algorithms (EAs). According to the no free lunch theorem, it is impossible for a single constraint handling technique to outperform all other techniques on every problem. In other words, depending on several factors such as the ratio between feasible search space and the whole search space, multimodality of the problem, the chosen EA, and global exploration/local exploitation stages of the search process, different constraint handling methods can be effective during different stages of the search process. Motivated by these observations, we propose an ensemble of constraint handling techniques (ECHT) to solve constrained real-parameter optimization problems, where each constraint handling method has its own population. A distinguishing feature of the ECHT is the usage of every function call by each population associated with each constraint handling technique. Being a general concept, the ECHT can be realized with any existing EA. In this paper, we present two instantiations of the ECHT using four constraint handling methods with the evolutionary programming and differential evolution as the EAs. Experimental results show that the performance of ECHT is better than each single constraint handling method used to form the ensemble with the respective EA, and competitive to the state-of-the-art algorithms.

Essentials of Constraint Programming

Abstract: 1. Introduction.- I. Constraint Programming.- 2. Algorithm = Logic + Control.- 3. Preliminaries of Syntax and Semantics.- 4. Logic Programming.- 5. Constraint Logic Programming.- 6. Concurrent Constraint Logic Programming.- 7. Constraint Handling Rules.- II. Constraint Systems.- 8. Constraint Systems and Constraint Solvers.- 9. Boolean Algebra B.- 10. Rational Trees RT.- 11. Linear Polynomial Equations R.- 12. Finite Domains FD.- 13. Non-linear Equations I.- III. Applications.- 14. Market Overview.- 15. Optimal Sender Placement for Wireless Communication.- 16. The Munich Rent Advisor.- 17. University Course Timetabling.- IV. Appendix.- A. Foundations from Logic.- A.1 First-Order Logic: Syntax and Semantics.- A.2 Basic Calculi and Normal Forms.- A.2.1 Substitutions.- A.2.2 Negation Normal Form and Prenex Form.- A.2.3 Skolemization.- A.2.4 Clauses.- A.2.5 Resolution.- List of Figures.- References.

ISAC --Instance-Specific Algorithm Configuration

Abstract: We present a new method for instance-specific algorithm configuration (ISAC). It is based on the integration of the algorithm configuration system GGA and the recently proposed stochastic offline programming paradigm. ISAC is provided a solver with categorical, ordinal, and/or continuous parameters, a training benchmark set of input instances for that solver, and an algorithm that computes a feature vector that characterizes any given instance. ISAC then provides high quality parameter settings for any new input instance. Experiments on a variety of different constrained optimization and constraint satisfaction solvers show that automatic algorithm configuration vastly outperforms manual tuning. Moreover, we show that instance-specific tuning frequently leads to significant speed-ups over instance-oblivious configurations.

Finite Model Theory and Its Applications

Abstract: Unifying Themes in Finite Model Theory.- On the Expressive Power of Logics on Finite Models.- Finite Model Theory and Descriptive Complexity.- Logic and Random Structures.- Embedded Finite Models and Constraint Databases.- A Logical Approach to Constraint Satisfaction.- Local Variations on a Loose Theme: Modal Logic and Decidability.

The complexity of temporal constraint satisfaction problems

Abstract: A temporal constraint language is a set of relations that has a first-order definition in(Q;

The SMACH High-Level Executive

Abstract: Personal robotics applications often require the integration of hundreds of components. In robot operating systems (ROSs), such subsystems and primitive capabilities are usually encapsulated in ROS nodes. Even with encapsulation and well-documented messaging interfaces, writing maintainable code to make a large set of ROS nodes to act together to solve a problem is difficult. Solution strategies range from writing code in big if/else cascades and nested switch statements to using more powerful inference and task-planning systems. In this column, we introduce an approach based on nested state machines that has proven very effective at building real-ROS applications. Complex Appications Over the past couple of years, we have been exploring the trade-offs between task scripting and task planning for highlevel control in robot applications written on top of ROS. Scripting approaches let the programmer not only say exactly what the robot should do, but also require the programmer to explicitly describe recovery logic for all failure modes. Although these methods can be rapid for developing small applications, they do not scale well. When failures arise, robots are not like pure software systems: they cannot just reset the state of the world and retry. As a result, autonomous robotics applications require a large amount of additional work to describe how to recover from these failures in addition to the application’s nominal execution. Furthermore, our experience has shown that maintaining, extending, and fixing such scripts over time makes it more and more challenging to analyze or model the application. On the other end of the spectrum, instead of explicitly describing which actions to execute in an imperative programming language, more autonomy can be given to the robot to plan and execute tasks. There exist model-based task planning and inference systems based on classic artificial intelligence (AI), constraint satisfaction, and model checking. The model, in this case, describes constraints and relations relevant to the set of actions at the robot’s disposal. These systems aim to shift the burden of solving the application-specific problems from the developer to the autonomous system.

A systematic approach to MDD-based constraint programming

Abstract: Fixed-width MDDs were introduced recently as a more refined alternative for the domain store to represent partial solutions to CSPs. In this work, we present a systematic approach to MDD-based constraint programming. First, we introduce a generic scheme for constraint propagation in MDDs. We show that all previously known propagation algorithms for MDDs can be expressed using this scheme. Moreover, we use the scheme to produce algorithms for a number of other constraints, including Among, Element, and unary resource constraints. Finally, we discuss an implementation of our MDD-based CP solver, and provide experimental evidence of the benefits of MDD-based constraint programming.

A general, fully distributed multi-agent planning algorithm

Abstract: We present a fully distributed multi-agent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods to take advantage of the structure of the underlying planning problem, m multi-agent planning domains with limited agent interaction, our algorithm empirically shows scalability beyond state of the art centralized solvers. Our work also provides a novel, real-world setting for testing and evaluating distributed constraint satisfaction algorithms in structured domains and illustrates how existing techniques can be altered to address such structure.

Constraint satisfaction techniques in planning and scheduling

Abstract: Over the last few years constraint satisfaction, planning, and scheduling have received increased attention, and substantial effort has been invested in exploiting constraint satisfaction techniques when solving real life planning and scheduling problems. Constraint satisfaction is the process of finding a solution to a set of constraints. Planning is the process of finding a sequence of actions that transfer the world from some initial state to a desired state. Scheduling is the problem of assigning a set of tasks to a set of resources subject to a set of constraints. In this paper, we introduce the main definitions and techniques of constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view.

As time goes by: Constraint Handling Rules: A survey of CHR research from 1998 to 2007

Abstract: Constraint Handling Rules (CHR) is a high-level programming language based on multiheaded multiset rewrite rules. Originally designed for writing user-defined constraint solvers, it is now recognized as an elegant general purpose language. Constraint Handling Rules related research has surged during the decade following the previous survey by Fruhwirth (J. Logic Programming, Special Issue on Constraint Logic Programming, 1998, vol. 37, nos. 1–3, pp. 95–138). Covering more than 180 publications, this new survey provides an overview of recent results in a wide range of research areas, from semantics and analysis to systems, extensions, and applications.

Solving subgraph isomorphism problems with constraint programming

Abstract: The subgraph isomorphism problem consists in deciding if there exists a copy of a pattern graph in a target graph. We introduce in this paper a global constraint and an associated filtering algorithm to solve this problem within the context of constraint programming. The main idea of the filtering algorithm is to label every node with respect to its relationships with other nodes of the graph, and to define a partial order on these labels in order to express compatibility of labels for subgraph isomorphism. This partial order over labels is used to filter domains. Labelings can also be strengthened by adding information from the labels of neighbors. Such a strengthening can be applied iteratively until a fixpoint is reached. Practical experiments illustrate that our new filtering approach is more effective on difficult instances of scale free graphs than state-of-the-art algorithms and other constraint programming approaches.

Revisiting the Linear Programming Relaxation Approach to Gibbs Energy Minimization and Weighted Constraint Satisfaction

Abstract: We present a number of contributions to the LP relaxation approach to weighted constraint satisfaction (= Gibbs energy minimization). We link this approach to many works from constraint programming, which relation has so far been ignored in machine vision and learning. While the approach has been mostly considered only for binary constraints, we generalize it to n-ary constraints in a simple and natural way. This includes a simple algorithm to minimize the LP-based upper bound, n-ary max-sum diffusion-however, we consider using other bound-optimizing algorithms as well. The diffusion iteration is tractable for a certain class of high-arity constraints represented as a black box, which is analogical to propagators for global constraints CSP. Diffusion exactly solves permuted n-ary supermodular problems. A hierarchy of gradually tighter LP relaxations is obtained simply by adding various zero constraints and coupling them in various ways to existing constraints. Zero constraints can be added incrementally, which leads to a cutting-plane algorithm. The separation problem is formulated as finding an unsatisfiable subproblem of a CSP.

Efficient Decision Makings for Dynamic Weapon-Target Assignment by Virtual Permutation and Tabu Search Heuristics

Abstract: The dynamic weapon-target assignment (DWTA) problem is a typical constrained combinatorial optimization problem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic asset-based DWTA model is established, especially for the warfare scenario of force coordination, to formulate this problem. Four categories of constraints, involving capability constraints, strategy constraints, resource constraints (i.e., ammunition constraints), and engagement feasibility constraints, are taken into account in the DWTA model. The concept of virtual permutation (VP) is proposed to facilitate the generation of feasible decisions. A construction procedure (CP) converts VPs into feasible DWTA decisions. With constraint satisfaction guaranteed by the synergy of VPs and the CP, an elaborate local search (LS) operator, namely move-to-head operator, is constructed to avoid repeatedly generating the same decisions. The operator is integrated into two tabu search (TS) algorithms to solve DWTA problems. Comparative experiments involving a random sampling method, an LS method, a hybrid genetic algorithm, a hybrid ant-colony optimization algorithm, and our TS algorithms show that the proposed TS heuristics for DWTA outperform their competitors in most test cases and they are competent for high-quality real-time DWTA decision makings.

Constraint programming for data mining and machine learning

Abstract: Machine learning and data mining have become aware that using constraints when learning patterns and rules can be very useful. To this end, a large number of special purpose systems and techniques have been developed for solving such constraint-based mining and learning problems. These techniques have, so far, been developed independently of the general purpose tools and principles of constraint programming known within the field of artificial intelligence. This paper shows that off-the-shelf constraint programming techniques can be applied to various pattern mining and rule learning problems (cf. also (De Raedt, Guns, and Nijssen 2008; Nijssen, Guns, and De Raedt 2009)). This does not only lead to methodologies that are more general and flexible, but also provides new insights into the underlying mining problems that allow us to improve the state-of-the-art in data mining. Such a combination of constraint programming and data mining raises a number of interesting new questions and challenges.

Constraint programming for mining n-ary patterns

Abstract: The aim of this paper is to model and mine patterns combining several local patterns (n-ary patterns). First, the user expresses his/her query under constraints involving n-ary patterns. Second, a constraint solver generates the correct and complete set of solutions. This approach enables to model in a flexible way sets of constraints combining several local patterns and it leads to discover patterns of higher level. Experiments show the feasibility and the interest of our approach.

Robust Constraint Satisfaction for Continuous-Time Nonlinear Systems in Strict Feedback Form

Abstract: This work is concerned with the design of feedback control laws that guarantee satisfaction of hard output constraints. A recursive procedure is used to derive sufficient conditions for robust constraint satisfaction in input affine nonlinear systems with bounded disturbances or parametric uncertainties. The control objective is achieved by a suitable switching between two control modes, one responsible for stabilization and one for constraint satisfaction.

Mapping extended feature models to constraint logic programming over finite domains

Abstract: As feature models for realistic product families may be quite complicated, automated analysis of feature models is desirable. Although several approaches reported in the literature addressed this issue, complex featureattribute and attribute-attribute relationships in extended feature models were not handled effectively. In this article, we introduce a mapping from extended feature models to constraint logic programming over finite domains. This mapping is used to translate basic, cardinality-based, and extended feature models, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships, into constraint logic programs. It thus enables use of offthe-shelf constraint solvers for the automated analysis of extended feature models involving such complex relationships. We also briefly discuss the ramifications of including feature-attribute relationships in operations of analysis. We believe that this proposal will be effective for further leveraging of constraint logic programming for automated analysis of feature models.

Integrating Operations Research in Constraint Programming

Abstract: This paper presents Constraint Programming as a natural formalism for modelling problems, and as a flexible platform for solving them. CP has a range of techniques for handling constraints including several forms of propagation and tailored algorithms for global constraints. It also allows linear programming to be combined with propagation and novel and varied search techniques which can be easily expressed in CP. The paper describes how CP can be used to exploit linear programming within different kinds of hybrid algorithm. In particular it can enhance techniques such as Lagrangian relaxation, Benders decomposition and column generation.

Adopting co-evolution and constraint-satisfaction concept on genetic algorithms to solve supply chain network design problems

Abstract: With the rapid globalization of markets, integrating supply chain technology has become increasingly complex. That is, most supply chains are no longer limited to a particular region. Because the numbers of branch nodes of supply chains have increased, products and raw materials vary and resource constraints differ. Thus, integrating planning mechanisms should include the capacity to respond to change. In the past, mathematical programming and a general heuristics algorithm were used to solve globalized supply chain network design problems. When mathematical programming is used to solve a problem and the number of decision variables is too high or constraint conditions are too complex, computation time is long, resulting in low efficiency, and can easily become trapped in partial optimum solution. When a general heuristics algorithm is used and the number of variables and constraints is too high, the degree of complexity increases. This usually results in an inability of people to think about resource constraints of enterprises and obtain an optimum solution. Therefore, this study uses genetic algorithms with optimum search features. This work combines the co-evolutionary mode, which is in accordance with various criteria and evolves dynamically, and constraint-satisfaction mode capacity to narrow the search space, which helps in finding rapidly a solution that, solves supply chain integration network design problems. Additionally, via mathematical programming, a simple genetic algorithm, co-evolutionary genetic algorithm, constraint-satisfaction genetic algorithm and co-evolutionary constraint genetic algorithm are used to compare the experiments result and processing time to confirm the performance of the proposed method.

On Learning Constraint Problems

Abstract: It is well known that modeling with constraints networks require a fair expertise. Thus tools able to automatically generate such networks have gained a major interest. The major contribution of this paper is to set a new framework based on Inductive Logic Programming able to build a constraint model from solutions and non-solutions of related problems. The model is expressed in a middle-level modeling language. On this particular relational learning problem, traditional top-down search methods fall into blind search and bottom-up search methods produce too expensive coverage tests. Recent works in Inductive Logic Programming about phase transition and crossing plateau shows that no general solution can face all these difficulties. In this context, we have designed an algorithm combining the major qualities of these two types of search techniques. We present experimental results on some benchmarks ranging from puzzles to scheduling problems.

Constant-Rank Condition and Second-Order Constraint Qualification

Abstract: The constant-rank condition for feasible points of nonlinear programming problems was defined by Janin (Math. Program. Study 21:127–138, 1984). In that paper, the author proved that the constant-rank condition is a first-order constraint qualification. In this work, we prove that the constant-rank condition is also a second-order constraint qualification. We define other second-order constraint qualifications.

Constraint programming and combinatorial optimisation in numberjack

Abstract: Numberjack is a modelling package written in Python for embedding constraint programming and combinatorial optimisation into larger applications. It has been designed to seamlessly and efficiently support a number of underlying combinatorial solvers. This paper illustrates many of the features of Numberjack through the use of several combinatorial optimisation problems.

A SAT-based Framework for Efficient Constrained Clustering.

Abstract: The area of clustering under constraints has recently received much attention in the data mining community. However, most work involves adding constraints to existing algorithms which, although being quite pragmatic, raises several difficulties. Examples of these difficulties include creating intractable constraint satisfaction sub-problems and constrained clustering algorithms that are easily over-constrained so they may not converge or converge to a poor clustering solution. In this paper we show how both instance and cluster-level constraints can be expressed as instances of the 2SAT problem and how multiple calls to a 2SAT solver can be used to construct algorithms that are guaranteed to satisfy all the constraints and converge to a global optimum for a number of intuitive objective functions. Our approach provides two additional advantages. Firstly, it leads to polynomial time algorithms for the k = 2 case for several objective functions. Secondly, one can specify large sets of constraints without fear of over-constraining the problem: if one or more solutions satisfying all constraints exist, our algorithm is guaranteed to find a best such solution. We present experimental results to show that our approach outperforms several popular algorithms particularly for large constraint sets, where these algorithms are over-constrained and fair poorly.