Dealing with incomplete knowledge on CLP(
            <i>FD</i>
            ) variable domains

Gavanelli, Marco; Lamma, Evelina; Mello, Paola; Milano, Michela

doi:10.1145/1057387.1057389

Cited by 7 publications

(16 citation statements)

References 26 publications

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“…Another related work is [18], which enables the user to define open domains, i.e., domains where values do not need to be explicitly and exhaustively listed, but their elements can be acquired along the computation, when needed. This approach could be useful in a future implementation of type extensions as part of the system MiniZinc.…”

Section: Related Workmentioning

confidence: 99%

Two type extensions for the constraint modeling language MiniZinc

Caballero

Stuckey

Tenorio-Fornés

2015

Science of Computer Programming

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In this paper we present two type extensions for the modelling language MiniZinc that allow the representation of some problems in a more natural way. The first proposal, called MiniZinc , extends existing types with additional values. The user can specify both the extension of a predefined type with new values, and the behavior of the operations with relation to the new types. We illustrate the usage of MiniZinc to model SQL-like problems with integer variables extended with NULL values. The second extension, MiniZinc + , introduces union types in the language. This allows defining recursive types such as trees, which are very useful for modelling problems that involve complex structures. A new case statement is introduced to select the different components of union type terms. The paper shows how a model defined using these extensions can be transformed into a MiniZinc model which is equivalent to the original model.

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Section: Related Workmentioning

confidence: 99%

Two type extensions for the constraint modeling language MiniZinc

Caballero

Stuckey

Tenorio-Fornés

2015

Science of Computer Programming

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“…The classical CSP model is then extended to an Interactive CSP [46], with corresponding solving algorithms. A corresponding CLP language [88] uses sets to represent the domains of FD variables. Later on, a general integration of the two sorts was proposed [50,18], which integrates sets and finite domain variables to speedup the CLP(SET ) computation.…”

Section: Setsmentioning

confidence: 99%

Constraint Logic Programming

Gavanelli¹,

Rossi²

2010

Lecture Notes in Computer Science

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Abstract. Constraint Logic Programming (CLP) is one of the most successful branches of Logic Programming; it attracts the interest of theoreticians and practitioners, and it is currently used in many commercial applications. Since the original proposal, it has developed enormously: many languages and systems are now available either as open source programs or as commercial systems.Also, CLP has been one of the technologies able to recruit researchers from other communities to the declarative programming cause. Current CLP engines include technologies and results developed in other communities, which themselves discovered logic as an invaluable tool to model and solve real-life problems. The CLP ParadigmConstraint Logic Programming (CLP) [7] represents a successful attempt to merge the best features of logic programming (LP) and constraint solving.Constraint solving [127,6,56,31] includes a variety of expressive modelling frameworks and efficient solving tools for real-life problems that can be described via a set of variables and constraints over them. A constraint is just a restriction imposed over the combination of values of some variables of the problem. Solving a problem with constraints means finding a way to assign values to all its variables such that all constraints are satisfied. Constraint solving methods have been successfully applied to many application domains, such as scheduling, planning, resource allocation, vehicle routing, computer networks, and bioinformatics [137,127,51].Embedding the notion of constraint into a high-level programming language allows for a more flexible and practical constraint processing environment, where constraints can be represented as formulae and can be incrementally accumulated. Moreover, the presence of constraints in a programming language usually augments its expressive power, in the sense that some complex relations can be defined easily by means of constraints, and there are also efficient techniques to prove them.For these reasons, constraints have been embedded in many programming environments, but some are more suitable than others. For example, the fact that constraints can be seen as relations or predicates, that constraint solving can be seen as a generalized form of unification, that their conjunction can be seen as a logical and, and that backtracking search is the base methodology to A. Dovier, E. Pontelli (Eds.): 25

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“…For this reason, we proposed (Gavanelli et al 2004) an extension, for the partially known case, of the concept of consistency, called known consistency. In this paper, we provide only the definition of node and arc-consistency; the extension to higher degrees of consistency is straightforward.…”

Section: The Fd Sortmentioning

confidence: 99%

“…Elements can then be removed (i.e., prevented from entering the current domain) if they are shown to be inconsistent. An algorithm for achieving KAC is shown in (Gavanelli et al 2004). …”

Section: Fig 1 Fd Constraints As Filters On Variable Domainsmentioning

confidence: 99%

A CHR-based implementation of known arc-consistency

Alberti¹,

Gavanelli²,

Lamma³

et al. 2005

Theory and Practice of Logic Programming

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In classical CLP(FD) systems, domains of variables are completely known at the beginning of the constraint propagation process. However, in systems interacting with an external environment, acquiring the whole domains of variables before the beginning of constraint propagation may cause waste of computation time, or even obsolescence of the acquired data at the time of use.For such cases, the Interactive Constraint Satisfaction Problem (ICSP) model has been proposed (Cucchiara et al. 1999a) as an extension of the CSP model, to make it possible to start constraint propagation even when domains are not fully known, performing acquisition of domain elements only when necessary, and without the need for restarting the propagation after every acquisition.In this paper, we show how a solver for the two sorted CLP language, defined in previous work (Gavanelli et al. 2004) to express ICSPs, has been implemented in the Constraint Handling Rules (CHR) language, a declarative language particularly suitable for high level implementation of constraint solvers.

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Dealing with incomplete knowledge on CLP( FD ) variable domains

Cited by 7 publications

References 26 publications

Two type extensions for the constraint modeling language MiniZinc

Two type extensions for the constraint modeling language MiniZinc

Constraint Logic Programming

A CHR-based implementation of known arc-consistency

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