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On minimal constraint networks

Artificial Intelligence 191-192 (C):42-60 (2012)

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  1. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems.Alan K. Mackworth & Eugene C. Freuder - 1985 - Artificial Intelligence 25 (1):65-74.
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  • Decomposing constraint satisfaction problems using database techniques.Marc Gyssens, Peter G. Jeavons & David A. Cohen - 1994 - Artificial Intelligence 66 (1):57-89.
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  • Relational Hidden Variables and Non-Locality.Samson Abramsky - 2013 - Studia Logica 101 (2):411-452.
    We use a simple relational framework to develop the key notions and results on hidden variables and non-locality. The extensive literature on these topics in the foundations of quantum mechanics is couched in terms of probabilistic models, and properties such as locality and no-signalling are formulated probabilistically. We show that to a remarkable extent, the main structure of the theory, through the major No-Go theorems and beyond, survives intact under the replacement of probability distributions by mere relations.
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  • On minimal constraint networks.Georg Gottlob - 2012 - Artificial Intelligence 191-192 (C):42-60.
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  • A comparison of structural CSP decomposition methods.Georg Gottlob, Nicola Leone & Francesco Scarcello - 2000 - Artificial Intelligence 124 (2):243-282.
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  • Structure identification in relational data.Rina Dechter & Judea Pearl - 1992 - Artificial Intelligence 58 (1-3):237-270.
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  • From local to global consistency.Rina Dechter - 1992 - Artificial Intelligence 55 (1):87-107.
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