The computational power and complexity of constraint handling rules

Sneyers, Jon; Schrijvers, Tom; Demoen, Bart

doi:10.1145/1462166.1462169

Cited by 40 publications

(52 citation statements)

References 32 publications

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“…However, these results are based on the theory of language embedding, developed in the field of concurrency theory to compare Turing complete languages, hence they [117], where the authors show that it is possible to implement any algorithm in CHR in an efficient way, i.e. with the best known time and space complexity.…”

Section: Summary and Related Workmentioning

confidence: 99%

“…Even though in [117] it is shown that any algorithm can be implemented in CHR preserving time and space complexity, yet in [75] it is claimed that "priorities do improve the expressivity of CHR".…”

Section: Chaptermentioning

confidence: 99%

“…Since all these languages are Turing powerful [117] in principle one can always encode a language into another one. The question is how difficult and how natural such an encoding is.…”

Section: Language Encodingmentioning

confidence: 99%

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Constraints Meet Concurrency

Mauro

2014

Atlantis Studies in Computing

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We investigate the benefits that emerge when the fields of constraint programming and concurrency meet. On one hand, constraints can be use in concurrency theory to increase the conciseness and the expressive power of concurrent languages from a pragmatic point of view. On the other hand, problems modeled by using constraints can be solved faster and more efficiently using a concurrent system. We explore both directions providing two separate lines of contribution. Firstly we study the expressive power of a concurrent language, namely Constraint Handling Rules, that supports constraints as a primitive construct. We show what features of this language make it Turing powerful. Then we propose a framework to solve constraint problems that is intended to be deployed on a concurrent system. For the development of this framework we used the concurrent language Jolie following the Service Oriented paradigm. Based on this experience, we also propose an extension to Service Oriented Languages to overcome some of their limitations and to improve the development of concurrent applications. Needless to say, I am also in debt with my coauthors:

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Section: Summary and Related Workmentioning

confidence: 99%

Section: Chaptermentioning

confidence: 99%

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Constraints Meet Concurrency

Mauro

2014

Atlantis Studies in Computing

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“…The classical optimal unionfind algorithm [TvL84] can be implemented in CHR with best-known quasilinear time complexity [SF06,SF05]. This result is not accidental, since the paper [SSD05] shows that every (RAM machine) algorithm with at least linear time complexity (an algorithm that at least reads all of its input), can be implemented in CHR with best known time and space complexity. Such a result is not known to hold in other pure declarative programming languages.…”

Section: Constraint Handling Rules (Chr)mentioning

confidence: 99%

Quasi-Linear-Time Algorithms by Generalisation of Union-Find in CHR

Frühwirth

2008

Lecture Notes in Computer Science

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Abstract. The union-find algorithm can be seen as solving simple equations between variables or constants. With a few lines of code change, we generalise its implementation in CHR from equality to arbitrary binary relations. By choosing the appropriate relations, we can derive fast incremental algorithms for solving certain propositional logic (SAT) problems and polynomial equations in two variables. In general, we prove that when the relations are bijective functions, our generalisation yields a correct algorithm. We also show that bijectivity is a necessary condition for correctness if the relations include the identity function.The rules of our generic algorithm have additional properties that make them suitable for incorporation into constraint solvers: from classical union-find, they inherit a compact solved form and quasi-linear time and space complexity. By nature of CHR, they are anytime and online algorithms. They solve and simplify the constraints in the problem, and can test them for entailment, even when the constraints arrive incrementally.

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“…This paper presents a novel method by directly adding different weights for the information gain equation of every candidate attribution, and this way not only makes the selection of the optimal attribute more reasonable, but also has a small effect on the running speed. Secondly, as we all know, in two equivalent formulas (e.g., the logarithmic expression and the four arithmetic operation), the computation speed of the logarithmic expression is slower than that of the four arithmetic operations that only include add, subtract, multiply and divide [27]. Hence, this paper changes the logarithmic expression of the information gain of ID3 into the form of the four arithmetic operations by introducing the Taylor formula for developing in real time.…”

Section: Introductionmentioning

confidence: 99%

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

Wang

Song

et al. 2017

Algorithms

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Abstract:The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field of classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing multi-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed that combines the simplified information entropy based on different weights with coordination degree in rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using three common data samples as well as the decision tree classifiers. It is shown that the proposed algorithm has a better performance in the running time and tree structure, but not in accuracy than the ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large, the proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and accuracy. The experimental results show that the proposed algorithm is effective and viable.

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The computational power and complexity of constraint handling rules

Cited by 40 publications

References 32 publications

Constraints Meet Concurrency

Constraints Meet Concurrency

Quasi-Linear-Time Algorithms by Generalisation of Union-Find in CHR

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

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