Constraint Handling Rules (CHR) is a high-level rule-based programming language which is increasingly used for general purposes. We introduce the CHR machine, a model of computation based on the operational semantics of CHR. Its computational power and time complexity properties are compared to those of the well-understood Turing machine and Random Access Memory machine. This allows us to prove the interesting result that every algorithm can be implemented in CHR with the best known time and space complexity. We also investigate the practical relevance of this result and the constant factors involved. Finally we expand the scope of the discussion to other (declarative) programming languages.

Constraint Handling Rules (CHR) is a committed-choice rule-based language that was originally intended for writing constraint solvers. In this paper we show that it is also possible to write the classic union-find algorithm and variants in CHR. The programs neither compromise in declarativeness nor e#ciency. We study the time complexity of our programs: they match the almost-linear complexity of the best known imperative implementations. This fact is illustrated with experimental results.

Constraint Handling Rules is a logical concurrent committedchoice rule-based language. Recently it was shown that the classical union-find algorithm can be implemented in CHR with optimal time complexity. Here we investigate if a parallel implementation of this algorithm is also possible in CHR. The problem is hard for several reasons:- Up to now, no parallel computation model for CHR was defined.- Tarjan’s optimal union-find is known to be hard to parallelize.- The parallel code should be as close as possible to the sequential one. It turns out that confluence analysis of the sequential implementation gives almost all the information needed to parallelize the union-find algorithm under a rather general parallel computation model for CHR.

Program analysis is essential for the optimized compilation of Constraint Handling Rules (CHRs) as well as the inference of behavioral properties such as confluence and termination. Up to now all program analyses for CHRs have been developed in an ad hoc fashion. In this work we bring the general program analysis methodology of abstract interpretation to CHRs: we formulate an abstract interpretation framework over the call-based operational semantics of CHRs. The abstract interpretation framework is non-obvious since it needs to handle the highly non-deterministic execution of CHRs. The use of the framework is illustrated with two instantiations: the CHR-specific late storage analysis and the more generally known groundness analysis. In addition, we discuss optimizations based on these analyses and present experimental results. Categories and Subject Descriptors D.3.2 [Programming Languages]: Language Classifications—Constraint and logic languages; F.3.2 [Logics and

Abstract. We motivate and develop a linear logic declarative semantics for CHR ∨ , an extension of the CHR programming language that integrates concurrent committed choice with backtrack search and a predefined underlying constraint handler. We show that our semantics maps each of these aspects of the language to a distinct aspect of linear logic. We show how we can use this semantics to reason about derivations in CHR ∨ and we present strong theorems concerning its soundness and completeness. 1

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Abstract. Tabled resolution, which alleviates some of Prolog’s termination problems, makes it possible to create practical applications from high-level declarative specifications. Constraint Handling Rules (CHR) is an elegant framework for implementing constraint solvers from high-level specifications, and is available in many Prolog systems. However, applications combining the power of these two declarative paradigms have been impractical since traditional CHR implementations interact poorly with tabling. In this paper we present a new (set-based) semantics for CHR which enables efficient integration with tabling. The new semantics coincides with the traditional (multi-set-based) semantics for a large class of CHR programs. We describe CHRd, an implementation based on the new semantics. CHRd uses a distributed constraint store that can be directly represented in tables. Although motivated by tabling, CHRd works well also on non-tabled platforms. We present experimental results which show that, relative to traditional implementations, CHRd performs significantly better on tabled programs, and yet shows comparable results on non-tabled benchmarks. 1

In this paper we introduce Associative Commutative Distributive Term Rewriting (ACDTR), a rewriting language for rewriting logical formulae. ACDTR extends AC term rewriting by adding distribution of conjunction over other operators. Conjunction is vital for expressive term rewriting systems since it allows us to require that multiple conditions hold for a term rewriting rule to be used. ACDTR uses the notion of a “conjunctive context”, which is the conjunction of constraints that must hold in the context of a term, to enable the programmer to write very expressive and targeted rewriting rules. ACDTR can be seen as a general logic programming language that extends Constraint Handling Rules and AC term rewriting. In this paper we define the semantics of ACDTR and describe our prototype implementation.

Constraint Handling Rules (CHR) is a high-level programming language based on multiheaded multiset rewrite rules. Originally designed for writing user-defined constraint solvers, it is now recognized as an elegant general purpose language. CHR-related research has surged during the decade following the previous survey by Frühwirth (1998). Covering more than 180 publications, this new survey provides an overview of recent results in a wide range of research areas, from semantics and analysis to systems, extensions and applications.

CHR (Constraint Handling Rules) is a committed-choice rulebased language that was originally intended for writing constraint solvers. Over time,