Reasoning on constraints in CLP(FD)

Constraint Logic Programming solvers on finite domains (CLP(FD) solvers) use constraints to prune those combinations of assignments which cannot appear in any consistent solution. There are applications, such as temporal reasoning or scheduling, requiring some form of qualitative reasoning where constraints can be changed (restricted) during the computation or even chosen when disjunction occurs. We embed in a (CLP(FD) solver the concept of constraints as first class objects. In the extended language, variables range over finite domains of objects (e.g., integers) and relation variables range over finite domains of relation symbols. We define operations and constraints on the two sorts of variables and one constraint linking the two. We first present the extension as a general framework, then we propose two specializations on finite domains of integers and of sets. Programming examples are given, showing the advantages of the extension proposed from both a knowledge representation and an operational viewpoint.     

A simple and efficient boolean solver for Constraint Logic Programming

We study in this paper the use of consistency techniques and local propagation methods, originally developed for constraints over finite domains, for solving boolean constraints in Constraint Logic Programming (CLP). To this aim, we first present a boolean CLP language clp(B/FD) built upon a CLP language over finite domains clp(FD) which uses a propagation-based constraint solver. It is based on a single primitive constraint which allows the boolean solver to be encoded at a low level. The boolean solver obtained in this way is both very simple and very efficient: on average it is eight times faster than the CHIP propagation-based boolean solver, i.e. nearly an order of magnitude faster, and infinitely better than the CHIP boolean unification solver. It also performs on average several times faster than special-purpose stand-alone boolean solvers. We then present in a second time several simplifications of the above approach, leading to the design of a very simple and compact dedicated boolean solver. This solver can be implemented in a WAM-based logical engine with a minimal extension limited to four new abstract instructions. This clp(B) system provides a further factor two speedup w.r.t. clp(B/FD).     

Extending constraint logic programming for temporal reasoning

In recent years, several constraint‐based 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’sPoint 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 interval‐based framework. This revised version was published online in June 2006 with corrections to the Cover Date.     

Nicolog: A simple yet powerful cc(FD) language

In this paper, we describe Nicolog, a language with capabilities similar to recently developed constraint logic programming (CLP) languages such as CLP(BNR), clp(FD), and cc(FD). Central to Nicolog are projection constraints (PCs), a sublanguage for compiling and optimizing constraint propagation in numeric and Boolean domains. PCs are an interesting generalization of the indexical constraints introduced in cc(FD) and also found in clp(FD). Nicolog compiles a very general class of built-in constraints into equivalent sets of PCs, allowing an arbitrary mixture of integer (easily extensible to real) and Boolean operations. Nicolog also lets the user program PCs directly, making it possible to implement new sophisticated propagation procedures. We show that PCs are a simple, efficient, and flexible way to implement most of the propagation procedures possible in other FD CLP systems. These include procedures for cardinality, constructive disjunction, implication, and mixed Boolean/numeric constraints. Empirical results with a simple prototype Nicolog implementation based on the WAM architecture show it can solve hard problems with speed comparable to the fastest existing CLP systems.     

A Comparative Study of Eight Constraint Programming Languages Over the Boolean and Finite Domains

This paper compares the efficiency of a number of Constraint Logic Programming (CLP) systems in the setting of finite domains as well as a specific aspect of their expressiveness (that concerning reification and meta-constraints). There are two key reasons for adopting CLP technology for solving a problem. The first is its expressiveness enabling a declarative solution with readable code which is vital for maintenance and the second is the provision of an efficient implementation for the computationally expensive procedures. However, CLP systems differ significantly both in how solutions may be expressed and the efficiency of their execution and it is important that both these factors are taken into account when choosing the best CLP system for a particular application. This paper aids this choice by illustrating differences between the systems, indicating their particular strengths and weaknesses.     

Constraint Logic Programming and Integer Programming approaches and their collaboration in solving an assignment scheduling problem

Generalised Assignment Problems (GAP), traditionally solved by Integer Programming techniques, are addressed in the light of current Constraint Programming methods. A scheduling application from manufacturing, based on a modified GAP, is used to examine the performance of each technique under a variety of problem characteristics. Experimental evidence showed that, for a set of assignment problems, Constraint Logic Programming (CLP) performed consistently better than Integer Programming (IP). Analysis of the CLP and IP processes identified ways in which the search was effective. The insight gained from the analysis led to an Integer Programming approach with significantly improved performance. Finally, the issue of collaboration between the two contrasting approaches is examined with respect to ways in which the solvers can be combined in an effective manner.     

Enhancing CLP branch and bound techniques for scheduling problems

In this paper, we propose a constraint logic programming (CLP) approach to the solution of a job shop scheduling problem in the field of production planning in orthopaedic hospital departments. A pure CLP on finite domain (CLP(FD)) approach to the problem has been developed, leading to disappointing results. In fact, although CLP(FD) has been recognized as a suitable tool for solving combinatorial problems, it presents some drawbacks for optimization problems. The main reason concerns the fact that CLP(FD) solvers do not effectively handle the objective function and cost‐based reasoning through the simple branch and bound scheme they embed. Therefore, we have proposed an improvement of the standard CLP branch and bound algorithm by exploiting some well‐known operations research results. The branch and bound we integrate in a CLP environment is based on the optimal solution of a relaxation of the original problem. In particular, the relaxation used for the job shop scheduling problem considered is the well‐known shifted bottleneck procedure considering single machine problems. The idea is to decompose the original problem into subproblems and solve each of them independently. Clearly, the solutions of each subproblem may violate constraints among different subproblems which are not taken into account. However, these solutions can be exploited in order to improve the pruning of the search space and to guide the search by defining cost‐based heuristics. The resulting algorithm achieves a significant improvement with respect to the pure CLP(FD) approach that enables the solution of problems which are one order of magnitude greater than those solved by a pure CLP(FD) algorithm. In addition, the resulting code is less dependent on the input data configuration. Copyright © 2001 John Wiley & Sons, Ltd.     

A hybrid approach to the single line scheduling problem with multiple products and sequence-dependent time

The present paper presents a hybrid approach for solving manufacturing scheduling problems, based on the integration between Constraint Logic Programming (CLP) and Genetic Algorithm (GA) approaches. The proposed methodology is applied to a single line with multiple products and sequence-dependent time. This system model derives from a real case of a company producing sheets for catalytic converters. A sensitivity analysis of the hybrid methodology is carried out to compare the performance of the CLP, GA and integrated CLP–GA approaches.     

Mixed Global Constraints and Inference in Hybrid CLP–IP Solvers

The complementing strengths of Constraint (Logic) Programming (CLP) and Mixed Integer Programming (IP) have recently received significant attention. Although various optimization and constraint programming packages at a first glance seem to support mixed models, the modeling and solution techniques encapsulated are still rudimentary. Apart from exchanging bounds for variables and objective, little is known of what constitutes a good hybrid model and how a hybrid solver can utilize the complementary strengths of inference and relaxations. This paper adds to the field by identifying constraints as the essential link between CLP and IP and introduces an algorithm for bidirectional inference through these constraints. Together with new search strategies for hybrid solvers and cut-generating mixed global constraints, solution speed is improved over both traditional IP codes and newer mixed solvers.     

用CLP技术解决动画自动生成中的布局规划问题*

以本实验室研制的一个多重论域的约束逻辑程序设计系统BPUCLP为基础,提出用约束逻辑程序(Constraint Logic Programming,CLP)解决布局规划问题。该方法用几何模型表示对象,用算术约束描述对象间的位置关系,并通过BPUCLP的约束求解机制为各个位置变量取值。该方法实现了二维人物初始布局规划和三维卧室家具布局规划。实验证明该方法是有效的。     

Computing weighted solutions in ASP: representation-based method vs. search-based method

For some problems with too many solutions, one way to obtain the more desirable solutions is to assign each solution a weight that characterizes its importance quantitatively, and then compute the solutions whose weights are over (resp. below) a given threshold. This paper studies computing weighted solutions for a given computational problem, in the context of Answer Set Programming (ASP). In particular, we investigate two sorts of methods for computing weighted solutions: one method suggests modifying the ASP representation of the problem to compute weighted solutions using an existing ASP solver and the other suggests modifying the search algorithm of the answer set solver to compute weighted solutions incrementally. The applicability of these methods are shown on two sorts of problems: reconstructing weighted phylogenies (for Indo-European languages and for Quercus species) and finding weighted plans (for Blocks World planning problems). In the experiments with the representation-based method, the answer set solver clasp is used and weight functions are represented in ASP. For the search-based method, the algorithm of clasp is modified (the modified solver is called clasp-w) and weight functions are implemented in C+?+. For phylogenies, two weight functions are introduced by incorporating domain-specific information about groupings of species; one of them cannot be represented in ASP due to some mathematical functions not supported by the ASP solvers. For plans, we define a weight function that characterizes the total cost of executing actions in a plan. In these experiments the following are observed. With weight measures that can be represented in ASP, the search-based method outperforms the representation-based method in terms of computational efficiency (both time and space). With weight functions that cannot be represented in ASP, the search-based method provides a tool for computing weighted solutions in ASP. The search-based method can be applied to different domains, without modifying the algorithm of clasp-w; in that sense, the search-based method is modular and can be useful to other ASP applications. With either method, plausible phylogenies among many can be found without computing all phylogenies and requiring historical linguists to go over them manually, and less costly plans can be found without computing all plans; in that sense, our methods contribute to phylogenetics and AI planning studies as well.     

Integrating constraint logic programming and operations research techniques for the Crew Rostering Problem

In this paper, we investigate the possibility of integrating Artificial Intelligence (AI) and Operations Research (OR) techniques for solving the Crew Rostering Problem (CRP). CRP calls for the optimal sequencing of a given set of duties into rosters satisfying a set of constraints. The optimality criterion requires the minimization of the number of crews needed to cover the duties. This kind of problem has been traditionally solved by OR techniques. In recent years, a new programming paradigm based on Logic Programming, named Constraint Logic Programming (CLP), has been successfully used for solving hard combinatorial optimization problems. CLP maintains all the advantages of logic programming such as declarativeness, non-determinism and an incremental style of programming, while overcoming its limitations, mainly due to the inefficiency in exploring the search space. CLP achieves good results on hard combinatorial optimization problems which, however, are not comparable with those achieved by OR approaches. Therefore, we integrate both techniques in order to design an effective heuristic algorithm for CRP which fully exploits the advantages of the two methodologies: on the one hand, we maintain the declarativeness of CLP, its ease of representing knowledge and its rapid prototyping; on the other hand, we inherit from OR some efficient procedures based on a mathematical approach to the problem. Finally, we compare the results we achieved by means of the integration with those obtained by a pure OR approach, showing that AI and OR techniques for hard combinatorial optimization problems can be effectively integrated. © 1998 John Wiley & Sons, Ltd.     

On the need for a different backtracking rule when dealing with late evaluation

Constraint Satisfaction Problems (CSPs) represent a widely used framework for many real-life problems. Constraint Logic Programming (CLP) languages are an effective tool for modeling problems in terms of CSPs and solving them efficiently.Lazy domain evaluation is a solving technique that has proven effective for solving CSPs, allowing the minimization of the number of constraint checks. However, exploiting lazy domain evaluation in CLP is not very effective, mainly because of the chronological backtracking rule used in CLP. After each backtracking step, in fact, all the obtained results are lost, even if they had nothing to do with the culprit of the failure. The intelligent backtracking rule widely studied in the past does not solve the problem either.In this paper, we propose a backtracking rule useful for dealing efficiently and declaratively with lazy domain evaluation in CLP, and we show a simple implementation of a metainterpreter providing the depicted functionality.     

Constructive negation and constraint logic programming with sets

The aim of this paper is to extend theConstructive Negation technique to the case ofCLP(SεT), a Constraint Logic Programming (CLP) language based on hereditarily (and hybrid) finite sets. The challenging aspects of the problem originate from the fact that the structure on whichCLP(SεT) is based is notadmissible closed, and this does not allow to reuse the results presented in the literature concerning the relationships betweenCLP and constructive negation. We propose a new constraint satisfaction algorithm, capable of correctly handling constructive negation for large classes ofCLP(SεT) programs; we also provide a syntactic characterization of such classes of programs. The resulting algorithm provides a novel constraint simplification procedure to handle constructive negation, suitable to theories where unification admits multiple most general unifiers. We also show, using a general result, that it is impossible to construct an interpreter forCLP(SεT) with constructive negation which is guaranteed to work for any arbitrary program; we identify classes of programs for which the implementation of the constructive negation technique is feasible. Agostino Dovier, Ph.D.: He is a researcher in the Department of Science and Technology at the University of Verona, Italy. He obtained his master degree in Computer Science from the University of Udine, Italy, in 1991 and his Ph.D. in Computer Science from the University of Pisa, Italy, in 1996. His research interests are in Programming Languages and Constraints over complex domains, such as Sets and Multisets. He has published over 20 research papers in International Journals and Conferences. He is teaching a course entitled “Special Languages and Techniques for Programming” at the University of Verona. Enrico Pontelli, Ph.D.: He is an Assistant Professor in the Department of Computer Science at the New Mexico State University. He obtained his Laurea degree from the University of Udine (Italy) in 1991, his Master degree from the University of Houston in 1992, and his Ph.D. degree from New Mexico State University in 1997. His research interests are in Programming Languages, Parallel Processing, and Constraint Programming. He has published over 50 papers and served on the program committees of different conferences. He is presently the Associate Director of the Laboratory for Logic, Databases, and Advanced Programming. Gianfranco Rossi, Ph.D.: He received his degree in Computer Science from the University of Pisa in 1979. From 1981 to 1983 he was employed at Intecs Co. System House in Pisa. From November 1983 to February 1989 he was a researcher at the Dipartimento di Informatica of the University of Turin. Since March 1989 he is an Associate Professor of Computer Science, currently with the University of Parma. He is the author of several papers dealing mainly with programming languages, in particular logic programming languages and Prolog, and extended unification algorithms. His current research interests are (logic) programming languages with sets and set unification algorithms.     

A practical object-oriented analysis engine for CLP

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.     

约束逻辑程序设计综述

一、引言 约束逻辑程序设计(Constraint Logic Program-ming．CLP)是基于人工智能(AI)中约束满足问题(Constraint Satisfaction Problem．CSP)模型的一种程序设计风范。CLP是逻辑程序设计(LP)的一种推广,是八十年代发展起来的一种新的逻辑程序设计方法。由于它继承了LP简单易懂的说明性描述方法并结合了CSP在求解问题时的效率,使它在解决很多AI问题(如组合问题、资源分配、事务安排等)时有不凡的表现。更由于AI领域中绝大多数问题可以用CLP来表示,所以这一方法已引起了人们的广泛注意,并在八十年代后期得以迅速发展。     

在ASP程序中实现IP地址和用户帐号的访问控制策略

本文就ASP技术中如何实现IP地址和用户帐号的访问控制给出了两种解决方案：一是在ASP程序中直接使用SQL查询命令,另一种是开发独立的服务器端DLL组件,然后嵌入到ASP程序中,两种方案均以详尽实例进行阐述。     

An investigation in parallel execution of answer set programs on distributed memory platforms: Task sharing and dynamic scheduling

Answer Set Programming (ASP) is a novel logic programming paradigm, that has already had a profound impact in several application domains, especially in the areas of knowledge representation and reasoning.In spite of the development of excellent inference engines for ASP, efficiency and scalability remain challenging aspects that prevent the use of ASP in various real-world domains. Parallelism has been identified as a natural avenue to address these problems.This paper describes the design of a complete ASP parallel engine, derived from the basic design of the Smodels architecture. The paper places emphasis on addressing the problem of the irregular structure of the search trees generated by typical ASP computations (in a Smodels-like computation), which requires the use of dynamic load balancing mechanisms. The paper provides a systematic investigation of alternative strategies for dynamic scheduling and task sharing. These are the two components that more directly affect the efficiency of a parallel engine.     

CLP(ℜ) and some electrical engineering problems

The Constraint Logic Programming Scheme defines a class of languages designed for programming with constraints using a logic programming approach. These languages are soundly based on a unified framework of formal semantics. In particular, as an instance of this scheme with real arithmetic constraints, the CLP() language facilitates and encourages a concise and declarative style of programming for problems involving a mix of numeric and non-numeric computation.In this paper we illustrate the practical applicability of CLP() with examples of programs to solve electrical engineering problems. This field is particularly rich in problems that are complex and largely numeric, enabling us to demonstrate a number of the unique features of CLP(). A detailed look at some of the more important programming techniques highlights the ability of CLP() to support well-known, powerful techniques from constraint programming. Our thesis is that CLP() is an embodiment of these techniques in a language that is more general, elegant and versatile than the earlier languages, and yet is practical.An earlier version of this paper appeared in the proceedings of the 4th International Conference on Logic Programming, Melbourne, May 1987. Much of this work was carried out while the authors were at Monash University, Melbourne, Australia.     

Weight constraint programs with evaluable functions

In the current practice of Answer Set Programming (ASP), evaluable functions are represented as special kinds of relations. This often makes the resulting program unnecessarily large when instantiated over a large domain. The extra constraints needed to enforce the relation as a function also make the logic program less transparent. In this paper, we consider adding evaluable functions to answer set logic programs. The class of logic programs that we consider here is that of weight constraint programs, which are widely used in ASP. We propose an answer set semantics to these extended weight constraint programs and define loop completion to characterize the semantics. Computationally, we provide a translation from loop completions of these programs to instances of the Constraint Satisfaction Problem (CSP) and use the off-the-shelf CSP solvers to compute the answer sets of these programs. A main advantage of this approach is that global constraints implemented in such CSP solvers become available to ASP. The approach also provides a new encoding for CSP problems in the style of weight constraint programs. We have implemented a prototype system based on these results, and our experiments show that this prototype system competes well with the state-of-the-art ASP solvers. In addition, we illustrate the utilities of global constraints in the ASP context.