Theory and practice of constraint handling rules

Frühwirth, Thom

doi:10.1016/s0743-1066(98)10005-5

Cited by 416 publications

(352 citation statements)

References 45 publications

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“…Constraints introduced in user query, by the conversion functions, and related to temporal entity types are evaluated after they are abducted. The prototype is implemented using constraint logic programming environment ECLiPSe (http://www.icparc.ic.ac.uk/eclipse/) with the extension of Constraint Handling Rules (CHR) [9]. Naturally, we use the constraint store to collect the abucibles; when a constraint is abducted, certain CHR rules will be triggered to simplify/propagate the constraint or signify a failure to cause backtracking of abduction.…”

Section: Mediation With Abductive Constraint Logic Programmingmentioning

confidence: 99%

Reasoning About Temporal Context Using Ontology and Abductive Constraint Logic Programming

Zhu¹,

Madnick²,

Siegel³

2004

Principles and Practice of Semantic Web Reasoning

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Abstract. The underlying assumptions for interpreting the meaning of data often change over time, which further complicates the problem of semantic heterogeneities among autonomous data sources. As an extension to the Context Interchange (COIN) framework, this paper introduces the notion of temporal context as a formalization of the problem. We represent temporal context as a multi-valued method in F-Logic; however, only one value is valid at any point in time, the determination of which is constrained by temporal relations. This representation is then mapped to an abductive constraint logic programming framework with temporal relations being treated as constraints. A mediation engine that implements the framework automatically detects and reconciles semantic differences at different times. We articulate that this extended COIN framework is suitable for reasoning on the Semantic Web.

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Section: Mediation With Abductive Constraint Logic Programmingmentioning

confidence: 99%

Reasoning About Temporal Context Using Ontology and Abductive Constraint Logic Programming

Zhu¹,

Madnick²,

Siegel³

2004

Principles and Practice of Semantic Web Reasoning

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“…In such systems, the user writes code in the host programming language of the solver and integrates it with the solver through an interface defining mainly how to interact with the variables and the core of the solver. Some solvers take a quite different approach and propose a language to define propagation, examples include constraint handling rules [11] and action rules [28]. Our language is a level of abstraction above those approaches, as it can be translated into code for any solver.…”

Section: Resultsmentioning

confidence: 99%

Towards Solver-Independent Propagators

Monette

Flener

Pearson

2012

Lecture Notes in Computer Science

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Abstract. We present an extension to indexicals to describe propagators for global constraints. The resulting language is compiled into actual propagators for different solvers, and is solver-independent. In addition, we show how this high-level description eases the proof of propagator properties, such as correctness and monotonicity. Experimental results show that propagators compiled from their indexical descriptions are sometimes not significantly slower than built-in propagators of Gecode. Therefore, our language can be used for the rapid prototyping of new global constraints.

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“…CHR [17] is a declarative, rule-based language for writing constraint solvers and is now included as an extension of several versions of Prolog. Operationally and implementation-wise, CHR extends Prolog with a constraint store, and the rules of a CHR program serve as rewriting rules over constraint stores.…”

Section: Constraint Handling Rules Chrmentioning

confidence: 99%

“…CHR is declarative in the sense that its rules can be understood as logical formulas. Constraint predicates must be declared as such and can then be called from a Prolog program; see [17] for details. The following example declares a constraint predicate a and defines a so-called propagation rule.…”

Section: Constraint Handling Rules Chrmentioning

confidence: 99%

HYPROLOG: A New Logic Programming Language with Assumptions and Abduction

Christiansen

Dahl

2005

Logic Programming

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Abstract. We present HYPROLOG, a novel integration of Prolog with assumptions and abduction which is implemented in and partly borrows syntax from Constraint Handling Rules (CHR) for integrity constraints. Assumptions are a mechanism inspired by linear logic and taken over from Assumption Grammars. The language shows a novel flexibility in the interaction between the different paradigms, including all additional built-in predicates and constraints solvers that may be available. Assumptions and abduction are especially useful for language processing, and we can show how HYPROLOG works seamlessly together with the grammar notation provided by the underlying Prolog system. An operational semantics is given which complies with standard declarative semantics for the "pure" sublanguages, while for the full HYPROLOG language, it must be taken as definition. The implementation is straightforward and seems to provide for abduction, the most efficient of known implementations; the price, however, is a limited use of negations. The main difference wrt. previous implementations of abduction is that we avoid any level of metainterpretation by having Prolog execute the deductive steps directly and by treating abducibles (and assumptions as well) as CHR constraints.

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Theory and practice of constraint handling rules

Cited by 416 publications

References 45 publications

Reasoning About Temporal Context Using Ontology and Abductive Constraint Logic Programming

Reasoning About Temporal Context Using Ontology and Abductive Constraint Logic Programming

Towards Solver-Independent Propagators

HYPROLOG: A New Logic Programming Language with Assumptions and Abduction

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