Constraint logic programming
01 Oct 1987-pp 111-119
TL;DR: In this article, a class of logic programming languages, called CLP languages, are defined, all of which share the same essential semantic properties and are based on a unified formal semantics.
Abstract: We address the problem of designing programming systems to reason with and about constraints. Taking a logic programming approach, we define a class of programming languages, the CLP languages, all of which share the same essential semantic properties. From a conceptual point of view, CLP programs are highly declarative and are soundly based within a unified framework of formal semantics. This framework not only subsumes that of logic programming, but satisfies the core properties of logic programs more naturally. From a user's point of view, CLP programs have great expressive power due to the constraints which they naturally manipulate. Intuition in the reasoning about programs is enhanced as a result of working directly in the intended domain of discourse. This contrasts with working in the Herbrand Universe wherein every semantic object has to be explicitly coded into a Herbrand term; this enforces reasoning at a primitive level. Finally, from an implementor's point of view, CLP systems can be efficient because of the exploitation of constraint solving techniques over specific domains.
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TL;DR: Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach to combining first-order logic and probabilistic graphical models in a single representation.
Abstract: We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.
2,916 citations
Book•
01 Jan 2003
TL;DR: Rina Dechter synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics to provide the first comprehensive examination of the theory that underlies constraint processing algorithms.
Abstract: Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning.
In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.
·Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challenges
·Builds the reader's understanding with definitions, examples, theory, algorithms and complexity analysis
·Synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics
Table of Contents
Preface; Introduction; Constraint Networks; Consistency-Enforcing Algorithms: Constraint Propagation; Directional Consistency; General Search Strategies; General Search Strategies: Look-Back; Local Search Algorithms; Advanced Consistency Methods; Tree-Decomposition Methods; Hybrid of Search and Inference: Time-Space Trade-offs; Tractable Constraint Languages; Temporal Constraint Networks; Constraint Optimization; Probabilistic Networks; Constraint Logic Programming; Bibliography
1,739 citations
TL;DR: This survey of CLP is to give a systematic description of the major trends in terms of common fundamental concepts and the three main parts cover the theory, implementation issues, and programming for applications.
Abstract: Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in different areas of applications. In this survey of CLP, a primary goal is to give a systematic description of the major trends in terms of common fundamental concepts. The three main parts cover the theory, implementation issues, and programming for applications.
1,571 citations
Book•
01 Jan 2006
TL;DR: Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.
Abstract: Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.
The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.
The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.
The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.
- Covers the whole field of constraint programming
- Survey-style chapters
- Five chapters on applications
Table of Contents
Foreword (Ugo Montanari)
Part I : Foundations
Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh)
Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth)
Chapter 3. Constraint Propagation (Christian Bessiere)
Chapter 4. Backtracking Search Algorithms (Peter van Beek)
Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang)
Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel)
Chapter 7. Tractable Structures for CSPs (Rina Dechter)
Chapter 8. The Complexity of Constraint Languages
(David Cohen, Peter Jeavons)
Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex)
Chapter 10. Symmetry in Constraint Programming
(Ian P. Gent, Karen E. Petrie, Jean-Francois Puget)
Chapter 11. Modelling (Barbara M. Smith)
Part II : Extensions, Languages, and Applications
Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace)
Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte)
Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson)
Chapter 15. Operations Research Methods in Constraint Programming (John Hooker)
Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers)
Chapter 17. Constraints over Structured Domains
(Carmen Gervet)
Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh)
Chapter 19. Temporal CSPs (Manolis Koubarakis)
Chapter 20. Distributed Constraint Programming
(Boi Faltings)
Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel)
Chapter 22. Constraint-Based Scheduling and Planning
(Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten)
Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw)
Chapter 24. Configuration (Ulrich Junker)
Chapter 25. Constraint Applications in Networks
(Helmut Simonis)
Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)
1,527 citations
TL;DR: How heuristic methods should be evaluated and proposed using the concept of Pareto optimality in the comparison of different heuristic approaches are discussed.
Abstract: This paper presents a survey of the research on the vehicle routing problem with time windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from one depot to a set of geographically scattered points. The routes must be designed in such a way that each point is visited only once by exactly one vehicle within a given time interval, all routes start and end at the depot, and the total demands of all points on one particular route must not exceed the capacity of the vehicle. Both traditional heuristic route construction methods and recent local search algorithms are examined. The basic features of each method are described, and experimental results for Solomon's benchmark test problems are presented and analyzed. Moreover, we discuss how heuristic methods should be evaluated and propose using the concept of Pareto optimality in the comparison of different heuristic approaches. The metaheuristic methods are described in the second part of this article.
1,103 citations
References
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TL;DR: An elaboration of the PROLOG language is described in which the notion of first-order term is replaced by a more general one, which allows the integration of inheritance directly into the unification process rather than indirectly through the resolutionbased inference mechanism of PROLOG.
Abstract: D An elaboration of the PROLOG language is described in which the notion of first-order term is replaced by a more general one This extended form of terms allows the integration of inheritance-an IS-A taxonomy-directly into the unification process rather than indirectly through the resolutionbased inference mechanism of PROLOG This results in more efficient computations and enhanced language expressiveness The language thus obtained, called LOGIN, subsumes PROLOG, in the sense that conventional PROLOG programs are equally well executed by LOGIN a
447 citations
01 Aug 1980
TL;DR: The goal approached is a complete programming system which will implicitly support the constraint paradigm to the same extent that LISP, say, supports automatic storage management.
Abstract: The constraint paradigm is a model of computation in which values are deduced whenever possible, but deductions must be {\it local}. One may visualize a constraint ``program" as a network of devices connected by wires. Data values may flow along the wires, and computation is performed by the devices, which use only locally available information (with a few exceptions), and places newly derived values on other, locally attached wires. In this way computed values are {\it propagated}. Advantages and disadvantages of the constraint paradigm are discussed, and a number of implementations of its programming languages are presented. The goal approached, though not quite reached, is a complete programming system which will implicitly support the constraint paradigm to the same extent that LISP, say, supports automatic storage management.
269 citations
TL;DR: It is proved that the intuitive notions of success and finite failure, defined in terms of T P, exactly correspond to the operational semantics.
47 citations
TL;DR: This is an advanced monograph in Combinatorics dealing with what quite often is called "Hungarian" combinatorics; hypergraphs, set systems, etc.
Abstract: This is an advanced monograph in Combinatorics dealing with what quite often is called "Hungarian" combinatorics; hypergraphs, set systems, etc. Presently, the connections of this part of Combinatorics to Artificial Intelligence and Databases are rather loose, the only application that comes to mind are topics related to cyclic and acyclic databases. Hence we should pose to ourselves the following question: is it worth it (from the professional point of view, of course) to read such books at all and is it worth it, in particular, to review them in a specialized bulletin like SIGART news? Generally the answer to both questions is, in the opinion of this reviewer, negative. Let us point out however that the combinatorics treated in Bollobas book is very interesting and sometimes stunning in its beauty. I wish programs (like AM, Eurisco, etc.) were able to produce such estetic results. For a moment, though, we have no hope of building programs mimicking inventiveness of mathematicians and, finally I do not expect to see such programs written during my lifespan.
44 citations