Abstract: This work investigates 3D geometric constraint solving for a representative class of basic problems that appear in practice as building blocks of more complex designs It shows that combining symbolic manipulation with homotopy continuation can be effective in solving certain families of problems In particular, polyhedral and parameter homotopies are shown to improve significantly the computation of the solutions by reducing considerably the number of paths to be tracked The choice of representation for the primitives and the order in which they are placed are shown to play a major role on the solution process The generic solutions for all tetrahedral problems are computed, and a systematic framework to solve octahedral problems involving points and planes is presented Two non trivial one-step constructions involving lines and spheres are also solved They correspond to the problems of placing a line tangent to 4 given spheres and the problem of Apollonius in 3D A family of problems involving 6 primitives, which can be points or lines is exhaustively studied They are shown to be intrinsically more complex and cannot be solved interactively with the current technological limitations This thesis also introduces a new terminology in geometric constraint solving which clearly distinguishes among the concepts of Abstract Constraint System, Realization, and Constraint Problem

Abstract: Schaefer showed, long ago, that there are, essentially, only three non-trivial classes of conjunctive Boolean formulae (or constraint satisfaction problems) for which oatisflability can be decided in polynomial time (assuming P $ NP), These three classes are LIN, 2-SAT and HORN-SAT. LIN is the constraint satisfaction problem in which all the constraints are linear equations modulo 2, 2-SAT is the constraint satisfaction problem in which all the constraints are disjunctions of at most two variables or their negations. HORN-SAT is the constraint satisfaction problem in which all the constraints are Horn clauses, i.e., disjunctions containing at most one negated variable. Given aatisfiable instances of LIN, P-SAT and HORNSAT, we can very efficiently find satisfying assignments. Suppose, however, that the instances that we are given are only almost-satisfiable, i.e., there are assignments that satisfy 1 e of their constraints, for some small con&ant e > 0, but no assignments that satisfy all their constraints. Can we efficiently find almost-satisfying assignments, i.e., assignments that satisfy 1 f(e) of the constraints, where f(c) is a function that tends to 0 an E tends to 01 For LIN, the answer turns out to be ‘no’. H&ad showed that, for any e > 0 and 6 > 0, finding an assignment hat satisfies l/2 -t 6 of the constraints of a (1 e)-satisfiable instance of LIN is NP-hard. In sharp contrast, we show here that the answer for 2SAT and HORN-SAT is ‘yes’. Given almost-satisfiable instances of P-SAT and of HORN-SAT we cun efhcicntly find almost-satisfying assignments. More specifically, given a (1 e)-satisfiable 2-SAT formula we can eflicicntly find a (1 O(e1/3))-satisfying assignment. Given a (1-c)-satisfiable HORN-SAT formula we can ‘Dopnrtment of Computer Science, School of Mathematical Bclenceo, Raymond and Beverly Sackler Faculty of Exact Scioncen, ‘Ibl Aviv University, Tel Aviv 69978, ISRAEL. Email: zuickdYnath.tau.ac.il. efficiently find a (1 O(loglog $/log $))-satisfying assignment. Our results, combined with extensions of Schaefer’s results obtained by Creignou and by Khanna, Sudan and Williamson, imply that ZSAT and HORN-SAT are, essentially, the only non-trivial classes of formulae for which almost-satisfying assignments can be found in polynomial time (assuming P # NP).

Abstract: 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.

Abstract: We provide here a proof theoretic account of constraint programming that attempts to capture the essential ingredients of this programming style. We exemplify it by presenting proof rules for linear constraints over interval domains, and illustrate their use by analyzing the constraint propagation process for the SEND + MORE = MONEY puzzle. We also show how this approach allows one to build new constraint solvers.

Abstract: A constraint violation stabilization technique for solving differential algebraic equations (DAE) of multibody dynamic systems is presented. The technique, based on the input ‐output feedback linearization, is employed to transform the nonlinear DAE into a set of linear equations. On reaching the input ‐output linear relationship with proven stable zero dynamics, a robust control design is adopted to construct constraint forces that can be used to effectively correct the errors accumulated in the constraint equations during the process of time integration. In the present development, if the pole placement method is used in control design of the resulting linear differential equations, constraint forces based on Baumgarte’ s constraint violation stabilization technique are recovered. On the other hand, if variable structure control design is adopted, a new method in calculating constraint forces is obtained. Two numerical examples are used to demonstrate the effectiveness of stabilizing the constraint violation by using the proposed technique.

Abstract: E-mail : fabdennad,marteg@informatik.uni-muenchen.de Abstract : Timetabling the courses offered at the Computer Science Department of the University of Munich requires the processing of hard and soft constraints. Hard constraints are conditions that must be satisfied, soft constraints however may be violated, but should be satisfied as much as possible. This paper shows how to model our timetabling problem as a partial constraint satisfaction problem and gives a concise finite domain solver implemented with Constraint Handling Rules that, by performing soft constraint propagation, allows for making soft constraints an active part of the problem solving process. Furthermore, we improve efficiency by reusing parts of the previous year’s timetable.

Abstract: Presents a new method for modeling and solving high-level synthesis problems. In our approach, finite-domain constraints and the related constraint-solving techniques offered by constraint logic programming are used. They make it possible to define basic constraints on operations and registers and provide a way to find optimal or suboptimal solutions to the data-path synthesis problem. Different design styles, such as multi-cycling, chaining, pipelined components and algorithmic pipelining can be modeled in this framework. The proposed formulation combines different constraints in one representation, and thus the optimization can find a better solution. The prototype system has been implemented in the constraint logic programming system CHIP. The extensive experiments carried out using this system have proved the feasibility of the presented approach.

Abstract: By focusing on the families of assignment and permutation problems (such as graph colouring and n-Queens), we show how to adapt D.R. Smith's (1990) KIDS approach for the synthesis of constraint programs (with implicit constraint satisfaction code), rather than applicative Refine programs with explicit constraint propagation and pruning code. Synthesis is guided by a global search schema and can be fully automated with little effort, due to some innovative ideas. CLP (Sets) programs are equivalent in expressiveness to our input specifications. The synthesised CLP (FD) programs would be, after optimising transformations, competitive with carefully hand-crafted ones.

Abstract: In this paper we present a technique for constraint allocation in analog system synthesis. Constraint allocation is the process of assigning constraint budgets to the subsystems so that the user asserted system level constraints are satisfied. Our approach is based on the formulation of the constraint allocation problem as a constraint satisfaction problem (CSP) and solving it. The solution method employed uses interval techniques to check for the satisfiability of the CSP. The generation of the exact set of solutions is done by an interval reduction and instantiation mechanism. We also discuss the constraint allocation mechanism in the context of a mixed-signal synthesis system. Finally, we present a design example to validate the constraint allocation technique.

Abstract: Invited Papers.- Open Constraint Programming.- Constructing Constraints.- The Dynamics of Dynamic Variable Ordering Heuristics.- Submitted Papers.- On Completion of Constraint Handling Rules.- Error-correcting Source Code.- Optimized Q-pivot for Exact Linear Solvers.- Constraint Techniques for Solving the Protein Structure Prediction Problem.- Global Constraints for Partial CSPs: A Case-Study of Resource and Due Date Constraints.- Using Graph Decomposition for Solving Continuous CSPs.- Anytime Lower Bounds for Constraint Violation Minimization Problems.- Introducing External Functions in Constraint Query Languages.- A Note on Partial Consistencies over Continuous Domains.- Consistency Techniques in Ordinary Differential Equations.- Early Projection in CLP(R).- Suggestion Strategies for Constraint-Based Matchmaker Agents.- Compiling Semiring-based Constraints with clp(FD,S).- Combining Topological and Qualitative Size Constraints for Spatial Reasoning.- Constraint Representation for Propagation.- A Unified Framework for Interval Constraints and Interval Arithmetic.- Constraint-based Problem Decomposition for a Key Configuration Problem.- Fuzzifying the Constraint Hierarchies Framework.- Constraints for Object Recognition in Aerial Images -Handling of Unobserved Features.- Salsa: A Language for Search Algorithms.- Random Constraint Satisfaction: theory meets practice.- A Tableau Based Constraint Solving Toolkit for Interactive Graphical Applications.- Safe Datalog Queries with Linear Constraints.- Non-systematic Search and Learning: An empirical study.- A Generic Model and Hybrid Algorithm for Hoist Scheduling Problems.- Linear concurrent constraint programming over reals.- Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems.- A Polynomial Time Local Propagation Algorithm for General Datafow Constraint Problems.- Stable Solutions for Dynamic Constraint Satisfaction Problems.- Posters.- Generation of Test Patterns for Differential Diagnosis of Digital Circuits.- Combine & Conquer: Genetic Algorithm and CP for Optimization.- Some Experiments on Learning Soft Constraints.- Scheduling Multi-Capacitated Resources under Complex Temporal Constraints.- Implementing Global Constraints with Index-Sets and Constraint Templates.- Generating feasible schedules for a pick-up and delivery problem.- An Impartial Efficiency Comparison of FD Constraint Systems.- Optimizing with constraints: a case study in scheduling maintenance of electric power units.- Some Surprising Regularities in the Behaviour of Stochastic Local Search.- Modelling CSP Solution Algorithms with Petri Decision Nets.- A Framework for Assertion-based Debugging in Constraint Logic Programming.- Parallel Execution Models for Constraint Propagation.- Using Blocks for Constraint Satisfaction.- Adaptive Solving of Equations over Rational Trees.- Telecommunication Application.- Optimal Placement of Base Stations in Wireless Indoor Telecommunication.

Abstract: This paper discusses the complexity of bound consistency on n-ary linear constraint system and investigates the relationship between equivalent binary equation systems from the perspective of bound consistency techniques. We propose an efficient bound consistency enforcing algorithm whose complexity is .In addition, by transforming a binary equation system into solved form, an efficient consistency enforcing algorithm can be achieved.

Abstract: . In the past we presented an algorithm to solve ordered constraint hierarchies based on a non-trivial error function using standard constraint satisfaction techniques. We extended this previous work and herewith present a new method to transform hierarchies of inequalities over the integers based on global comparators into equivalent ordered constraint hierarchies. The correctness of this method is proven. Using the results of our previous work, we present another method transforming the resulting ordered constraint hierarchies into ordinary constraint systems. These systems of algebraic equalities and inequalities over the integer domain are soluble with available finite-domain constraint solvers. Finally, we propose some modifications and simplifications of the considered constraint hierarchies, improving the search for solutions and being useful in practical applications like job-shop scheduling. The modifications proposed are the combination of consecutive hierarchy levels in one level where ...

Abstract: Given a problem represented as a set of constraints, the goal of constraint satisfaction is to find solutions that satisfy the constraints. A necessary requirement for constraint satisfaction is the discrimination of some potential solutions from others by investigating how well they satisfy constraints. Often, a practical constraint satisfaction problem involves a situation in which one potential solution is evaluated to be better than another if the former satisfies constraints significantly better than the latter does. Approximate constraint satisfaction aims at the selection of solutions or the seriation of potential solutions for such a problem in the presence of the notion of significant difference or "indifference interval". The underlying idea of approximate constraint satisfaction can be roughly stated that: (1) constraints under consideration are ordered by their importance in the selection of solutions; and (2) when potential solutions' satisfactions of a constraint are not significantly different, subsequent constraints are used to discriminate the potential solutions. Based on this idea, we propose two methods of approximate constraint satisfaction and discuss their properties.

Abstract: The natural representation of employee timetabling problems (ETPs), as constraint networks (CNs), has variables representing tasks and values representing employees that are assigned to tasks. In this representation, ETPs have binary constraints of non-equality (mutual exclusion), the networks are non uniform, and variables have different domains of values. There is also a typical family of non-binary constraints that represent finite capacity limits. These features differentiate the networks of ETPs from random uniform binary CNs. Much experimental work has been done in recent years on random binary constraint networks (cf. [10,11, 9]) and the so called phase transitions have been connected with certain value combinations of the parameters of random binary CNs. This paper designs and experiments with a random testbed of ETPs that includes all of the above features and is solved by standard constraint processing techniques, such as forward checking (FC) and conflict directed backjumping (CBJ). Random ETPs are characterized by the usual parameters of constraint networks, like the density of constraints p1. One result of the experiments is that random ETPs exhibit a strong change in difficulty, as measured by consistency checks, (a phase transition). The critical parameter for the observed phase transition is the average size of domains of variables. Non binary constraints of finite capacity are part of the experimental testbed. An enhanced FC-CBJ search algorithm is used to test these random networks and the experimental results are presented.

Abstract: Constraint satisfaction is at the core of many applications, such as scheduling. The study of phase transition has benefited algorithm selection and algorithm development in constraint satisfaction. Recent research provides evidence that constraint graph topology affects where phase transitions occur in constraint satisfaction problems. In this article, a new phase transition predictor which takes constraint graph information into consideration is proposed. The new predictor allows variation in the tightness of individual constraints and node degree variation in constraint graph. Experiments were conducted to study the usefulness of the new predictor on random binary constraint satisfaction problems. Results show that the new predictor is able to produce predictions as good as the state-of-the-art predictor in general, but do considerably better in sparsely constrained problems, particularly when the node degree variation in their constraint graphs is high.

Abstract: The author presents a solution to a variety of statics problems using a declarative and executable model. The flexibility of this model is its ability to answer a variety of queries about a physical mechanism, providing the means for simulation, control or evaluation. This model possesses a well-defined compositional operator by which physical systems may be assembled from constituent parts. The model is achieved by means of a rule-based and constraint-based language. Constraint solving aids in flexibility by providing equations and inequalities without a specified order of evaluation. Rules provide additional flexibility by allowing nondeterminism, while providing ease-of-use through a declarative description of the model.

Abstract: We introduce a constraint system LC that handles arithmetic constraints over reals within the linear concurrent constraint programming (lcc) framework. This approach provides us with a general, extensible foundation for linear programming algorithm design that comes with a (linear) logical semantics. In particular, it allows us to build a 'glass-box' version of the (constraint solver) simplex algorithm by defining (monotone) cc ask and tell agents over a higher-level constraint system as lcc(LC) programs. We illustrate at the same time the use of the lccframework as a non-trivial concurrent algorithm specification tool.

Abstract: In this paper, an architecture for the combination of different constraint solvers with the help of projections is proposed. By means of the definition of an interface and restricting properties of the operations of constraint solvers we are able to define a mechanism for the open and consistent combination of constraint systems. We enable the use of a functional logic language as constraint solver for constraints over functional expressions in the overall system. The syntax of a language which allows the specification of mixed constraints of different constraint domains and its operational semantics are introduced. We compare our approach with BALI, an environment for designing and executing constraint solver combinations.

Abstract: In assembly modelling, it is necessary to capture the relative positions of components in the assembly. Many such problems concern constraint representation and its manipulation such as constraint reduction. This paper formulates the constraints as groups of rigid body transformations and proposes a constraint reduction procedure, based on Lie algebra. Then, this approach is applied to the constraint representation and reduction in an assembly model.

Abstract: We observe a repeated-update problem in the process of updating walkabout strengths of the DeltaBlue algorithm, which is known as a fast constraint solving method based on local propagation. According to the basic references on the DeltaBlue algorithm, the time complexity of the planning phase is described as O(MN) for a constraint problem such that the number of constraints is N and the maximum number of methods a constraint has is M. We, however, point out that the time complexity is not O(MN) using a very simple example. In this example, the time complexity is exponential order for N. Then we present a corrected DeltaBlue algorithm whose time complexity is O(EN^2) for a constraint problem such that the number of constraints is N and the maximum number of variables a constraint has is E. Finally we measure the performance of the corrected DeltaBlue algorithm using two benchmarks.

Abstract: Based on the dynamic equation, the performance functional and the system constraint equation of time-invariant discrete LQ control problem, the generalized Riccati equations of linear equality constraint system are obtained according to the minimum principle, then a deep discussion about the above equations is given, and finally numerical example is shown in this paper.

Abstract: In order to improve the deductive power of ffinite domain constraint solvers usually redundant and global constraints are added to the constraint system. The objective of this work [2] is to develop a new constraint solving scheme designed as an extension of a classical arc-consistency algorithm. Associated to a declarative language, it allows the user to implement his own global relations without the need of manipulating internal structures of the solver or modifying in depth the original model of the studied problem. This system relies on two new structures: index-sets and constraint templates. The former consist in sets of integers used as indices over tables. They collect variables sharing some properties (for instance tasks in a scheduling problem assigned to the same machine). The latter are descriptions of constraints that must be applied (possibly) over selected sets of variables (for instance the fact that a set of tasks must be scheduled before another set). Both, set-index and constraint template deffnitions are evaluated dynamically and depend on the variables’ domains. Indeed, the new constraints are automatically generated by the constraint solver based on the set contents and variable domain values. The index-set and constraint deffinition language uses a mathematic style notation. The deffnitions are compiled to the solver representation which can be directly handled by the propagation mechanism. This organisation guarantees a high level of efficiency.

Abstract: We consider a new constraint for improved timing recovery in partial response digital magnetic recording. The one-pairs constraint is an upper bound on the maximum number of symbols between pairs of ones in a coded sequence and can be considered as an alternative to the widely-used (0,G/I) constraint. We derive the capacity of this constraint, compute the power spectral density for maxentropic one-pairs constrained codes and give rate 16/17 and 24/25 block codes for this constraint.

Abstract: In this paper we present a view of constraint programming based on the notion of rewriting controlled by strategies. We argue that this concept allows us to describe in a unified way the constraint solving mechanism as well as the meta-language needed to manipulate the constraints. This has the advantage to provide descriptions that are very close to the proof theoretical setting used now to describe constraint manipulations like unification or numerical constraint solving. We examplify the approach by presenting examples of constraint solvers descriptions and combinations written in the ELAN language.

Abstract: Automatic test data generation leads to identify input values on which a selected point in a procedure is executed. This paper introduces a new method for this problem based on constraint solving techniques. First, we statically transform a procedure into a constraint system by using well-known "Static Single Assignment" form and control-dependencies. Second, we solve this system to check whether at least one feasible control flow path going through the selected point exists and to generate test data that correspond to one of these paths.The key point of our approach is to take advantage of current advances in constraint techniques when solving the generated constraint system. Global constraints are used in a preliminary step to detect some of the non feasible paths. Partial consistency techniques are employed to reduce the domains of possible values of the test data. A prototype implementation has been developped on a restricted subset of the C language. Advantages of our approach are illustrated on a non-trivial example.

Abstract: It is well known that any non-binary discrete constraint satisfaction problem (CSP) can be translated into an equivalent binary CSP. Two translations are known: the dual graph translation and the hidden variable translation. However, there has been little theoretical or experimental work on how well backtracking algorithms perform on these binary representations in comparison to their performance on the corresponding non-binary CSP. We present both theoretical and empirical results to help understand the tradeoffs involved. In particular, we show that translating a non-binary CSP into a binary representation can be a viable solution technique in certain circumstances. The ultimate aim of this research is to give guidance for when one should consider translating between non-binary and binary representations. Our results supply some initial answers to this question.

Abstract: This paper proposes a number of models for integrating finitedomain stochastic constraint solvers into constraint logic programming systems to solve constraint-satisfaction problems efficiently. Stochastic solvers can solve hard constraint-satisfaction problems very efficiently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraints; hence their combination is attractive. Unfortunately, there is a mismatch between the kind of information a stochastic solver provides and that which a constraint logic programming system requires. We study the semantic properties of the various models of constraint logic programming systems that make use of stochastic solvers, and give soundness and completeness results for their use. We describe an example system we have implemented using a modified neural network simulator, GENET, as a constraint solver. We briefly compare the efficiency of these models against the propagation-based solver approaches that are typically used in constraint logic programming.