Arc and path consistency revisited

Mohr, Roger; Henderson, Thomas C.

doi:10.1016/0004-3702(86)90083-4

Cited by 439 publications

(173 citation statements)

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“…arc-consistency algorithm improving on the O(ed 2) bound of [16]. Although a better asymptotic complexity does not guarantee a faster algorithm, empirical and theoretical results suggest the practicability of our results.…”

Section: Our Results Indicate That Many Classes Of Constraints Lead Tmentioning

confidence: 86%

“…[2, 6-8, 11, 19]), preprocessing techniques, and constraint algorithms (see e.g. [12,14,16,18,27]) have been designed and analyzed for this class of problems (see the reviews [13,20] for a comprehensive overview of this area). In this paper, we are mainly concerned with (network) consistency techniques, and arc consistency in particular.…”

Section: Introductionmentioning

confidence: 99%

“…Arc-consistency algorithms work on binary CSP and make such that the constraints are individually consistent. Arc-consistency algorithms have a long history of their own; they originate from the Waltz filtering algorithm [27] and were refined several times [12] to culminate in the optimal algorithm AC-4 of Mohr and Henderson [16]. AC-4 runs in O(ed '-), where e is the number of arcs in the network and d is the size of the largest domain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A generic arc-consistency algorithm and its specializations

Hentenryck

Deville

Teng

1992

Artificial Intelligence

289

135

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Section: Our Results Indicate That Many Classes Of Constraints Lead Tmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A generic arc-consistency algorithm and its specializations

Hentenryck

Deville

Teng

1992

Artificial Intelligence

289

135

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“…Our language is also missing some constructs that are found in the implementation of constraints, such as entailment detection, watched literals [13], and fine-grained events [15]. Part of our future work will be dedicated to studying how these can be included without overcomplicating the language.…”

Section: Language Design Decisionsmentioning

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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“…The goal is to rule out some impossible values for some variables, using a collection of unary constraints (in_domain) and binary constraints (compatible) that are given by the problem and/or tested during backtracking search (see Figure 12). The "natural" forward-chaining execution strategy for this Dyna program corresponds to the classical, asymptotically optimal AC-4 algorithm [48]. % exceptions for base cases fib(1) := 1.…”

Section: Training Of Arithmetic Circuitsmentioning

confidence: 99%

Dyna: Extending Datalog for Modern AI

Eisner

Filardo

2011

Datalog Reloaded

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Abstract. Modern statistical AI systems are quite large and complex; this interferes with research, development, and education. We point out that most of the computation involves database-like queries and updates on complex views of the data. Specifically, recursive queries look up and aggregate relevant or potentially relevant values. If the results of these queries are memoized for reuse, the memos may need to be updated through change propagation. We propose a declarative language, which generalizes Datalog, to support this work in a generic way. Through examples, we show that a broad spectrum of AI algorithms can be concisely captured by writing down systems of equations in our notation. Many strategies could be used to actually solve those systems. Our examples motivate certain extensions to Datalog, which are connected to functional and object-oriented programming paradigms. Why a New Data-Oriented Language for AI?Modern AI systems are frustratingly big, making them time-consuming to engineer and difficult to modify. In this chapter, we describe our work toward a declarative language that was motivated originally by various use cases in AI. Our goal is to make it easier to specify a wide range of new systems that are more or less in the mold of existing AI systems. Our declarative language should simplify inferential computation in the same way that the declarative language of regular expressions has simplified string pattern matching and transduction.All areas of AI have become data-intensive, owing to the flood of data and the pervasiveness of statistical modeling and machine learning. A system's extensional data (inputs) include not only current sensory input but also background ⋆ This chapter has been condensed for publication; the full version is available as [22].

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Arc and path consistency revisited

Cited by 439 publications

References 6 publications

A generic arc-consistency algorithm and its specializations

A generic arc-consistency algorithm and its specializations

Towards Solver-Independent Propagators

Dyna: Extending Datalog for Modern AI

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