Local consistency in parallel constraint satisfaction networks

Simon Kasif; Arthur L. Delcher

doi:10.1016/0004-3702(94)90085-X

Local consistency in parallel constraint satisfaction networks

Simon Kasif, Arthur L. Delcher

School of Medicine

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

In this paper we present several basic techniques for achieving parallel execution of constraint networks. The major result supported by our investigations is that the parallel complexity of constraint networks is critically dependent on subtle properties of the network that do not influence its sequential complexity.

Original language	English (US)
Pages (from-to)	307-327
Number of pages	21
Journal	Artificial Intelligence
Volume	69
Issue number	1-2
DOIs	https://doi.org/10.1016/0004-3702(94)90085-X
State	Published - Sep 1994

ASJC Scopus subject areas

Language and Linguistics
Linguistics and Language
Artificial Intelligence

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title = "Local consistency in parallel constraint satisfaction networks",

abstract = "In this paper we present several basic techniques for achieving parallel execution of constraint networks. The major result supported by our investigations is that the parallel complexity of constraint networks is critically dependent on subtle properties of the network that do not influence its sequential complexity.",

author = "Simon Kasif and Delcher, {Arthur L.}",

note = "Funding Information: This research has been supported partially by the Air Force Office of Scientific Research under grant AFOSR-89-1151, the National Science Foundation under grant IRI-88-09324 and NSF/DARPA Grant CCR-8908092. Thanks are due to David McAllester, Judea Pearl and Rina Dechter for their constructive comments.",

year = "1994",

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doi = "10.1016/0004-3702(94)90085-X",

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AU - Delcher, Arthur L.

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PY - 1994/9

Y1 - 1994/9

N2 - In this paper we present several basic techniques for achieving parallel execution of constraint networks. The major result supported by our investigations is that the parallel complexity of constraint networks is critically dependent on subtle properties of the network that do not influence its sequential complexity.

AB - In this paper we present several basic techniques for achieving parallel execution of constraint networks. The major result supported by our investigations is that the parallel complexity of constraint networks is critically dependent on subtle properties of the network that do not influence its sequential complexity.

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VL - 69

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JO - Artificial Intelligence

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Local consistency in parallel constraint satisfaction networks

Abstract

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