Toupie: A constraint language for model checking

The incorporation of global program analysis into recent compilers for Constraint Logic Programming (CLP) languages has greatly improved the efficiency of compiled programs. We present a global analyser based on abstract interpretation. Unlike traditional optimizers, whose designs tend to be ad hoc, the analyser has been designed with flexibility in mind. The analyser is incremental, allowing substantial program transformations by a compiler without requiring redundant re-computation of analysis data. The analyser is also generic in that it can perform a large number of different program analyses. Furthermore, the analyser has an object-oriented design, enabling it to be adapted to different applications easily and allowing it to be used with various CLP languages with simple modifications. As an example of this generality, we sketch the use of the analyser in two different applications involving two distinct CLP languages: an optimizing compiler for CLP(R) programs and an application for detecting occur-check problems in Prolog programs. © 1998 John Wiley & Sons Ltd.

Constraint Logic Programming (CLP) languages extend logic programming by allowing the use of constraints from different domains such as real numbers or Boolean functions. They have proved to be ideal for expressing problems that require interactive mathematical modeling and complex combinatorial optimization problems. However, CLP languages have mainly been considered as research systems, useful for rapid prototyping, by not really competitive with more conventional programming languages where efficiency is a more important consideration. One promising approach to improving the performance of CLP systems is the use of powerful program optimizations to reduce the cost of constraint solving. We extend work in this area by describing a new optimizing compiler for the CLP language CLP(ℛ). The compiler implements six powerful optimizations: reordering of constraints, removal of redundant variables, and specialization of constraints which cannot fail. Each program optimization is designed to remove the overhead of constraint solving when possible and keep the number of constraints in the store as small as possible. We systematically evaluate the effectiveness of each optimization in isolation and in combination. Our empirical evaluation of the compiler verifies that optimizing compilation can be made efficient enough to allow compilation of real-world programs and that it is worth performing such compilation because it gives significant time and space performance improvements.

The concept of Expert Decision Support Systems is based on the integration of Decision Support Systems and Expert Systems, providing the decision maker with features from both types of systems (e.g. simulation, explanations, control of reasoning). Constraint logic programming languages, such as CLP(R) or CHIP, achieve this integration by merging logic programming, a paradigm of Artificial Intelligence and Expert Systems, with constraint solving techniques similar to those used in Operations Research and Decision Support Systems. Their embedded constraint solver extends the expressive power of logic programming languages, most notably on the domain of numerical expressions. The paper illustrates how constraint logic programming may be used in EDSS development by presenting a small classroom scheduling problem developed with CLP(R), and compares this approach to more traditional Operations Research techniques.

In recent years, several constraintdbased temporal reasoning frameworks have been proposed. They consider temporal points or intervals as domain elements linked by temporal constraints. Temporal reasoning in these systems is based on constraint propagation. In this paper, we argue that a language based on constraint propagation can be a suitable tool for expressing and reasoning about temporal problems. We concentrate on Constraint Logic Programming (CLP) which is a powerful programming paradigm combining the advantages of Logic Programming and the efficiency of constraint solving. However, CLP presents some limitations in dealing with temporal reasoning. First, it uses an “arc consistency” propagation algorithm which is embedded in the inference engine, cannot be changed by the user, and is too weak in many temporal frameworks. Second, CLP is not able to deal with qualitative temporal constraints. We present a general meta CLP architecture which maintains the advantages of CLP, but overcomes these two main limitations. Each architectural level is a finite domain constraint solver (CLP(FD)) that reasons about constraints of the underlying level. Based on this conceptual architecture, we extend the CLP(FD) language and we specialize the extension proposed on Vilain and Kautz’s Point Algebra, on Allen’s Interval Algebra and on the STP framework by Dechter, Meiri and Pearl. In particular, we show that we can cope effectively with disjunctive constraints even in an intervaldbased framework.

CAL: A theoretical background of constraint logic programming and its applications

A general constraint-based formulation for the analysis and partial synthesis of two-dimensional truss structures is presented. This formulation is general in that it handles statically determinate and statically indeterminate trusses with pin and roller supports, and concentrated joint loads. The formulation is constraint-based in that the physical behavior of truss components is declaratively represented using constraints.The analysis and partial synthesis of a truss structure manifest themselves in proving the satisfiability of the constraints associated with the structural components. An artificial intelligence approach called constraint logic programming is used for representing and satisfying constraints. A constraint logic programming language, called CLP(R), is used for implementing the formulation. The implemented program consists of sixteen rules. These rules are used for both the analysis and partial synthesis of truss structures. Several truss analysis and synthesis examples using the formulation are presented.

In this paper, we show a validity of a new programming paradigm called constraint logic programming by constructing a robot structure design support system. The design process of a robot structure is divided into a fundamental structure design and a internal structure design. A fundamental structure design is a process that determines a framework of the robot such as a degree of freedom, a number of joint, etc. We mainly support a fundamental structure design. This design system is constructed by constraint logic programming language. The Buchberger algorithm is used to calculate the nonlinear equation in this language. Our design system generates an analysis program independent of robot structure easily. The result of analysis is also gotten in the form of expression. The constraint can be appended incrementally. By constructing such a system, we can achieve remarkable reduction of designer's work in design process.

Handbook of Constraint Programming

The job of the constraint programmer is to use mathematical constraints to model real world constraints and objects. In this book, Kim Marriott and Peter Stuckey provide the first comprehensive introduction to the discipline of constraint programming and, in particular, constraint logic programming. The book covers the necessary background material from artificial intelligence, logic programming, operations research, and mathematical programming. Topics discussed range from constraint-solving techniques to programming methodologies for constraint programming languages. Because there is not yet a universally used syntax for constraint logic programming languages, the authors present the programs in a way that is independent of any existing programming language. Practical exercises cover how to use the book with a number of existing constraint languages.

We have described the constraint logic programming language CS-Prolog. CS-Prolog is written in PROLOG about 1,500 lines. This is a very small as oppose to similar system CLP which is written in C about 12,000 lines, but less efficient than CLP. However, CS-Prolog solves equations and inequations in reasonable time and this paper shows the simple approach to constraint logic programming using PROLOG and this technique is very useful for constraint logic programming language system on top of PROLOG machine.

In this paper, we present the constraint language Toupie which is a finite domain μ-calculus interpreter that uses extended decision diagrams to represent relations and formulae. “Classical” constraint logic programming languages over finite domains (CLP(FD)) are designed to find one solution to a constraint problem, eventually the best one according to a given criterion. In Toupie, constraints are used to characterize existing relationships between variables. We advocate the use of this paradigm to model and solve efficiently difficult constraint problems that are not tractable with CLP(FD) languages.KeywordsSymbolic Model CheckingConstraint Languages

In object-oriented representation, inheritance determines the attributes of a given object, given its type. Conversely, recognition determines the type of an object, given its attributes. The author addresses the more general problem of recognition from constraints; arguing that it can be seen as a special case of the classical abduction process and that it can be implemented as an abduction operation on constraints. Abductive reasoning is common in most fields of artificial intelligence. Constraint logic programming (CLP) generalizes logic programming by replacing unification with constraint solving. He first explores the relationship between abduction and CLP and shows that CLP is a special case of abduction. A class of problems is then identified for which the usual unrestricted forms of abduction are unsuitable. Within the CLP framework a restricted form of abduction is formulated which is applicable to the whole class of problems, in which object-oriented recognition can be considered as a special case. >

Using artificial neural networks for constraint satisfaction problem

Resource-constrained scheduling problems are commonly found in various areas, such as project management, manufacturing, transportation, software engineering, computer networks, and supply chain management. Its problem models involve a large number of constraints and discrete decision variables, including binary and integer. In effect, the representation of resource allocation, for instance, is often expressed using binary or integer decision variables to form several constraints according to the respective scheduling problem. It significantly increases the number of decision variables and constraints as the problem scales; such kind of traditional approaches based on operations research is insufficient. Therefore, a hybrid approach to decision support for resource-constrained scheduling problems which combines operation research (OR) and constraint logic programming (CLP) is proposed. Unlike OR-based approaches, declarative CLP provides a natural representation of different types of constraints. This approach provides: (a) decision support through the answers to the general and specific questions, (b) specification of the problem based on a set of facts and constraints, (c) reduction to the combinatorial solution space. To evaluate efficiency and applicability of the proposed hybrid approach and implementation platform, implementation examples of job-shop scheduling problem are presented separately for the three environments, i.e., Mathematical Programming (MP), CLP, and hybrid implementation platform.

The Constraint Satisfaction Problem (CSP) formalization has been a productive tool within Artificial Intelligence and related areas. The Finite CSP (FCSP) framework is presented here as a restricted logical calculus within a space of logical representation and reasoning systems. FCSP is formulated in a variety of logical settings: theorem proving in first order predicate calculus, propositional theorem proving (and hence SAT), the Prolog and Datalog approaches, constraint network algorithms, a logical interpreter for networks of constraints, the Constraint Logic Programming (CLP) paradigm and propositional model finding (and hence SAT, again). Several standard, and some not-so-standard, logical methods can therefore be used to solve these problems. By doing this we obtain a specification of the semantics of the common approaches. This synthetic treatment also allows algorithms and results from these disparate areas to be imported, and specialized, to FCSP; the special properties of FCSP are exploited to achieve, for example, completeness and to improve efficiency. It also allows export to the related areas. By casting CSP both as a generalization of FCSP and as a specialization of CLP it is observed that some, but not all, FCSP techniques lift to CSP and, perhaps, thereby to CLP. Various new connections are uncovered, in particular between the proof-finding approaches and the alternative model-finding approaches that have arisen in depiction and diagnosis applications.