scholarly journals Constraint Problem Specification as Compression

10.29007/7ths ◽  
2018 ◽  
Author(s):  
Steve Prestwich ◽  
S. Armagan Tarim ◽  
Roberto Rossi
Keyword(s):  
Logic Programming ◽  
Data Compression ◽  
Constraint Programming ◽  
Auxiliary Problem ◽  
Combinatorial Problems ◽  
Specification Languages ◽  
Constraint Problem ◽  
High Level ◽  
Natural Way ◽  
Comparable Size

Constraint Programming is a powerful and expressive framework for modelling and solving combinatorial problems. It is nevertheless not always easy to use, which has led to the development of high-level specification languages. We show that Constraint Logic Programming can be used as a meta-language to describe itself more compactly at a higher level of abstraction. This can produce problem descriptions of comparable size to those in existing specification languages, via techniques similar to those used in data compression. An advantage over existing specification languages is that, for a problem whose specification requires the solution of an auxiliary problem, a single specification can unify the two problems. Moreover, using a symbolic representation of domain values leads to a natural way of modelling channelling constraints.

ECLiPSe – From LP to CLP

2011 ◽  
Vol 12 (1-2) ◽  
pp. 127-156 ◽  
Author(s):  
JOACHIM SCHIMPF ◽  
KISH SHEN
Keyword(s):  
Logic Programming ◽  
Constraint Programming ◽  
Combinatorial Problem ◽  
Third Party ◽  
Programming System ◽  
Development Environment ◽  
Control Language ◽  
Extended Prolog ◽  
High Level ◽  
And Control

AbstractECLiPSe is a Prolog-based programming system, aimed at the development and deployment of constraint programming applications. It is also used for teaching most aspects of combinatorial problem solving, for example, problem modelling, constraint programming, mathematical programming and search techniques. It uses an extended Prolog as its high-level modelling and control language, complemented by several constraint solver libraries, interfaces to third-party solvers, an integrated development environment and interfaces for embedding into host environments. This paper discusses language extensions, implementation aspects, components, and tools that we consider relevant on the way from Logic Programming to Constraint Logic Programming.

scholarly journals From Support Propagation to Belief Propagation in Constraint Programming

2019 ◽  
Vol 66 ◽  
Author(s):  
Gilles Pesant
Keyword(s):  
Constraint Programming ◽  
Belief Propagation ◽  
Combinatorial Problems ◽  
Global Constraints ◽  
Inference Algorithms ◽  
Shared Variables ◽  
Shared Information ◽  
Individual Variables ◽  
High Level ◽  
Search Guidance

The distinctive driving force of constraint programming to solve combinatorial problems has been a privileged access to problem structure through the high-level models it uses. From that exposed structure in the form of so-called global constraints, powerful inference algorithms have shared information between constraints by propagating it through shared variables’ domains, traditionally by removing unsupported values. This paper investigates a richer propagation medium made possible by recent work on counting solutions inside constraints. Beliefs about individual variable-value assignments are exchanged between contraints and iteratively adjusted. It generalizes standard support propagation and aims to converge to the true marginal distributions of the solutions over individual variables. Its advantage over standard belief propagation is that the higher-level models featuring large-arity (global) constraints do not tend to create as many cycles, which are known to be problematic for convergence. The necessary architectural changes to a constraint programming solver are described and an empirical study of the proposal is conducted on its implementation. We find that it provides close approximations to the true marginals and that it significantly improves search guidance.

scholarly journals Constraint Programming for an Efficient and Flexible Block Modeling Solver

2020 ◽  
Vol 34 (09) ◽  
pp. 13685-13688
Author(s):  
Alex Lucía Mattenet ◽  
Ian Davidson ◽  
Siegfried Nijssen ◽  
Pierre Schaus
Keyword(s):  
Constraint Programming ◽  
Search Tree ◽  
Combinatorial Problems ◽  
Block Modeling ◽  
Clustering Problem ◽  
Constraint Language ◽  
Spatio Temporal ◽  
Feasible Solutions ◽  
High Level ◽  
Temporal Data Analysis

Constraint Programming (CP) is a powerful paradigm for solving combinatorial problems. In CP, the user creates a model by declaring variables with their domains and expresses the constraints that need to be satisfied in any solution. The solver is then in charge of finding feasible solutions—a value in the domain of each variable that satisfies all the constraints. The discovery of solutions is done by exploring a search tree that is pruned by the constraints in charge of removing impossible values. The CP framework has the advantage of exposing a rich high-level declarative constraint language for modeling, as well as efficient purpose-specific filtering algorithms that can be reused in many problems. In this work, we harness this flexibility and efficiency for the Block Modeling problem. It is a variant of the graph clustering problem that has been used extensively in many domains including social science, spatio-temporal data analysis and even medical imaging. We present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches. We show its use in the analysis of real datasets.

scholarly journals From Support Propagation to Belief Propagation in Constraint Programming (Extended Abstract)

2020 ◽  
Author(s):  
Gilles Pesant
Keyword(s):  
Constraint Programming ◽  
Driving Force ◽  
Belief Propagation ◽  
Combinatorial Problems ◽  
Problem Structure ◽  
Privileged Access ◽  
Propagation Medium ◽  
High Level ◽  
Search Guidance ◽  
Counting Solutions

The distinctive driving force of constraint programming (CP) to solve combinatorial problems has been a privileged access to problem structure through the high-level models it uses. We investigate a richer propagation medium for CP made possible by recent work on counting solutions inside constraints. Beliefs about individual variable-value assignments are exchanged between contraints and iteratively adjusted. Its advantage over standard belief propagation is that the higher-level models do not tend to create as many cycles, which are known to be problematic for convergence. We find that it significantly improves search guidance.

scholarly journals Modular action language

2015 ◽  
Vol 16 (2) ◽  
pp. 189-235 ◽  
Author(s):  
DANIELA INCLEZAN ◽  
MICHAEL GELFOND
Keyword(s):  
Logic Programming ◽  
Medium Size ◽  
Programming System ◽  
Problem Structuring ◽  
System Description ◽  
Action Languages ◽  
Special Cases ◽  
Action Language ◽  
Stepwise Development ◽  
High Level

AbstractThe paper introduces a new modular action language,${\mathcal ALM}$, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993,Journal of Logic Programming 17, 2–4, 301–321; 1998,Electronic Transactions on AI 3, 16, 193–210) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that requirestructuring of knowledge.$\mathcal{ALM}$is meant to remedy this problem. Structuring of knowledge in${\mathcal ALM}$is supported by the concepts ofmodule(a formal description of a specific piece of knowledge packaged as a unit),module hierarchy, andlibrary, and by the division of a system description of${\mathcal ALM}$into two parts:theoryandstructure. Atheoryconsists of one or more modules with a common theme, possibly organized into a module hierarchy based on adependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them.Structuresare used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries.

Declarative Planning and Knowledge Representation in an Action Language

2011 ◽  
pp. 192-221 ◽  
Author(s):  
Thomas Eiter ◽  
Wolfgang Faber ◽  
Gerald Pfeifer
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
Planning System ◽  
Epistemic State ◽  
Action Languages ◽  
Knowledge States ◽  
Action Language ◽  
High Level ◽  
Planning Problems ◽  
Complex Planning

This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation & Reasoning, action languages like K allow the formalization of complex planning problems involving non-determinism and incomplete knowledge in a very flexible manner. By giving an overview of existing planning languages and comparing these against our language, we aim on further promoting the applicability and usefulness of high-level action languages in the area of planning. As opposed to previously existing languages for modeling actions and change, K adopts a logic programming view where fluents representing the epistemic state of an agent might be true, false or undefined in each state. We will show that this view of knowledge states can be fruitfully applied to several well-known planning domains from the literature as well as novel planning domains. Remarkably, K often allows to model problems more concisely than previous action languages. All the examples given can be tested in an available implementation, the DLVK planning system.

scholarly journals A new constraint programming model and solving for the cyclic hoist scheduling problem

Constraints ◽  
2020 ◽  
Vol 25 (3-4) ◽  
pp. 319-337 ◽  
Author(s):  
Mark Wallace ◽  
Neil Yorke-Smith
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
State Of The Art ◽  
Scale Up ◽  
Generic Model ◽  
Scheduling Problem ◽  
Hoist Scheduling ◽  
Wide Range ◽  
Constraint Programming Model ◽  
High Level

AbstractThe cyclic hoist scheduling problem (CHSP) is a well-studied optimisation problem due to its importance in industry. Despite the wide range of solving techniques applied to the CHSP and its variants, the models have remained complicated and inflexible, or have failed to scale up with larger problem instances. This article re-examines modelling of the CHSP and proposes a new simple, flexible constraint programming formulation. We compare current state-of-the-art solving technologies on this formulation, and show that modelling in a high-level constraint language, MiniZinc, leads to both a simple, generic model and to computational results that outperform the state of the art. We further demonstrate that combining integer programming and lazy clause generation, using the multiple cores of modern processors, has potential to improve over either solving approach alone.

Animated fuzzy logic

1998 ◽  
Vol 8 (5) ◽  
pp. 503-525 ◽  
Author(s):  
GARY MEEHAN ◽  
MIKE JOY
Keyword(s):  
Fuzzy Logic ◽  
Functional Language ◽  
Fuzzy Subset ◽  
High Level ◽  
Natural Way

In this paper we aim to give an introduction to fuzzy logic using the language Haskell to implement our solutions. We shall see how the high-level, declarative nature of a functional language allows us to implement easily and efficiently solutions to problems using fuzzy logic and, in particular, how the presence of functions as first-class values allows us to model the key concept of the fuzzy subset in a natural way.

Constraint logic programming

1991 ◽  
Vol 6 (3) ◽  
pp. 151-194 ◽  
Author(s):  
Pascal Van Hentenryck
Keyword(s):  
Logic Programming ◽  
Programming Languages ◽  
Constraint Logic Programming ◽  
Combinatorial Problems ◽  
Basic Operation ◽  
Combinatorial Search ◽  
New Class ◽  
Constraint Systems ◽  
Declarative Semantics ◽  
Relational Form

AbstractConstraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from their underlying theory, to their constraint systems, and to their applications to combinatorial problems.

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