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  1. Thick set inversion.B. Desrochers & L. Jaulin - 2017 - Artificial Intelligence 249:1-18.
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  • Filtering algorithms for the multiset ordering constraint.Alan M. Frisch, Brahim Hnich, Zeynep Kiziltan, Ian Miguel & Toby Walsh - 2009 - Artificial Intelligence 173 (2):299-328.
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  • 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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  • Constraint propagation with interval labels.Ernest Davis - 1987 - Artificial Intelligence 32 (3):281-331.
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  • Learning to improve constraint-based scheduling.Monte Zweben, Eugene Davis, Brian Daun, Ellen Drascher, Michael Deale & Megan Eskey - 1992 - Artificial Intelligence 58 (1-3):271-296.
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  • Arc-consistency and arc-consistency again.Christian Bessière - 1994 - Artificial Intelligence 65 (1):179-190.
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  • Arc-consistency for continuous variables.Boi Faltings - 1994 - Artificial Intelligence 65 (2):363-376.
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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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  • Investigating production system representations for non-combinatorial match.Milind Tambe & Paul S. Rosenbloom - 1994 - Artificial Intelligence 68 (1):155-199.
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  • Constraints, consistency and closure.Peter Jeavons, David Cohen & Martin C. Cooper - 1998 - Artificial Intelligence 101 (1-2):251-265.
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  • Combining topological and size information for spatial reasoning.Alfonso Gerevini & Jochen Renz - 2002 - Artificial Intelligence 137 (1-2):1-42.
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  • Multi-agent oriented constraint satisfaction.Jiming Liu, Han Jing & Y. Y. Tang - 2002 - Artificial Intelligence 136 (1):101-144.
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  • The Arthur Prior memorial conference, Christchurch, 1989.B. J. Copeland & D. R. Murdoch - 1991 - Journal of Symbolic Logic 56 (1):372-382.
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  • Theoretical analysis of singleton arc consistency and its extensions.Christian Bessiere & Romuald Debruyne - 2008 - Artificial Intelligence 172 (1):29-41.
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  • Using constraint metaknowledge to reduce arc consistency computation.Christian Bessiére, Eugene C. Freuder & Jean-Charles Regin - 1999 - Artificial Intelligence 107 (1):125-148.
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  • Backtracking algorithms for disjunctions of temporal constraints.Kostas Stergiou & Manolis Koubarakis - 2000 - Artificial Intelligence 120 (1):81-117.
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  • Solving connected row convex constraints by variable elimination.Yuanlin Zhang & Satyanarayana Marisetti - 2009 - Artificial Intelligence 173 (12-13):1204-1219.
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  • Properties of tree convex constraints.Yuanlin Zhang & Eugene C. Freuder - 2008 - Artificial Intelligence 172 (12-13):1605-1612.
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  • Multi-agent path finding with mutex propagation.Han Zhang, Jiaoyang Li, Pavel Surynek, T. K. Satish Kumar & Sven Koenig - 2022 - Artificial Intelligence 311 (C):103766.
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  • A theory of conflict resolution in planning.Qiang Yang - 1992 - Artificial Intelligence 58 (1-3):361-392.
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  • Random constraint satisfaction: Easy generation of hard (satisfiable) instances.Ke Xu, Frédéric Boussemart, Fred Hemery & Christophe Lecoutre - 2007 - Artificial Intelligence 171 (8-9):514-534.
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  • Qualitative reasoning with directional relations.D. Wolter & J. H. Lee - 2010 - Artificial Intelligence 174 (18):1498-1507.
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  • Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving.Richard J. Wallace & Eugene C. Freuder - 2005 - Artificial Intelligence 161 (1-2):209-227.
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  • Reasoning about qualitative temporal information.Peter van Beek - 1992 - Artificial Intelligence 58 (1-3):297-326.
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  • Constraint satisfaction using constraint logic programming.Pascal Van Hentenryck, Helmut Simonis & Mehmet Dincbas - 1992 - Artificial Intelligence 58 (1-3):113-159.
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  • A generic arc-consistency algorithm and its specializations.Pascal Van Hentenryck, Yves Deville & Choh-Man Teng - 1992 - Artificial Intelligence 57 (2-3):291-321.
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  • A gentle introduction to Numerica.Pascal Van Hentenryck - 1998 - Artificial Intelligence 103 (1-2):209-235.
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  • No more “Partial” and “Full Looking Ahead”.Edward Tsang - 1998 - Artificial Intelligence 98 (1-2):351-361.
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  • Planning with constraints.Mark Stefik - 1981 - Artificial Intelligence 16 (2):111-139.
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  • Parsing as non-Horn deduction.Edward P. Stabler - 1993 - Artificial Intelligence 63 (1-2):225-264.
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  • Asynchronous aggregation and consistency in distributed constraint satisfaction.Marius-Călin Silaghi & Boi Faltings - 2005 - Artificial Intelligence 161 (1-2):25-53.
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  • The increasing cost tree search for optimal multi-agent pathfinding.Guni Sharon, Roni Stern, Meir Goldenberg & Ariel Felner - 2013 - Artificial Intelligence 195 (C):470-495.
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  • (1 other version)A Probabilistic Constraints Approach to Language Acquisition and Processing.Mark S. Seidenberg & Maryellen C. MacDonald - 1999 - Cognitive Science 23 (4):569-588.
    This article provides an overview of a probabilistic constraints framework for thinking about language acquisition and processing. The generative approach attempts to characterize knowledge of language (i.e., competence grammar) and then asks how this knowledge is acquired and used. Our approach is performance oriented: the goal is to explain how people comprehend and produce utterances and how children acquire this skill. Use of language involves exploiting multiple probabilistic constraints over various types of linguistic and nonlinguistic information. Acquisition is the process (...)
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  • Spatial relation learning for explainable image classification and annotation in critical applications.Régis Pierrard, Jean-Philippe Poli & Céline Hudelot - 2021 - Artificial Intelligence 292 (C):103434.
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  • Modelling and solving temporal reasoning as propositional satisfiability.Duc Nghia Pham, John Thornton & Abdul Sattar - 2008 - Artificial Intelligence 172 (15):1752-1782.
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  • Arc consistency for factorable relations.Mark Perlin - 1992 - Artificial Intelligence 53 (2-3):329-342.
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  • Consistent-labeling problems and their algorithms: Expected-complexities and theory-based heuristics.Bernard Nudel - 1983 - Artificial Intelligence 21 (1-2):135-178.
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  • Constraint propagation as information maximization.A. Nait Abdallah & M. H. van Emden - 2013 - Artificial Intelligence 197 (C):25-38.
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  • Qualitative reasoning about relative direction of oriented points.Till Mossakowski & Reinhard Moratz - 2012 - Artificial Intelligence 180-181 (C):34-45.
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  • A condensed semantics for qualitative spatial reasoning about oriented straight line segments.Reinhard Moratz, Dominik Lücke & Till Mossakowski - 2011 - Artificial Intelligence 175 (16-17):2099-2127.
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  • Constraint relaxation may be perfect.Ugo Montanari & Francesca Rossi - 1991 - Artificial Intelligence 48 (2):143-170.
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  • Arc and path consistency revisited.Roger Mohr & Thomas C. Henderson - 1986 - Artificial Intelligence 28 (2):225-233.
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  • Fuzzy rrDFCSP and planning.Ian Miguel & Qiang Shen - 2003 - Artificial Intelligence 148 (1-2):11-52.
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  • Combining qualitative and quantitative constraints in temporal reasoning.Itay Meiri - 1996 - Artificial Intelligence 87 (1-2):343-385.
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  • The logic of constraint satisfaction.Alan K. Mackworth - 1992 - Artificial Intelligence 58 (1-3):3-20.
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  • The complexity of constraint satisfaction revisited.Alan K. Mackworth & Eugene C. Freuder - 1993 - Artificial Intelligence 59 (1-2):57-62.
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  • A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments.Xudong Luo, Nicholas R. Jennings, Nigel Shadbolt, Ho-Fung Leung & Jimmy Ho-man Lee - 2003 - Artificial Intelligence 148 (1-2):53-102.
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  • A spectrum of compromise aggregation operators for multi-attribute decision making.Xudong Luo & Nicholas R. Jennings - 2007 - Artificial Intelligence 171 (2-3):161-184.
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  • Variable-Centered Consistency in Model RB.Liang Li, Tian Liu & Ke Xu - 2013 - Minds and Machines 23 (1):95-103.
    Model RB is a model of random constraint satisfaction problems, which exhibits exact satisfiability phase transition and many hard instances, both experimentally and theoretically. Benchmarks based on Model RB have been successfully used by various international algorithm competitions and many research papers. In a previous work, Xu and Li defined two notions called i-constraint assignment tuple and flawed i-constraint assignment tuple to show an exponential resolution complexity of Model RB. These two notions are similar to some kind of consistency in (...)
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