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  1. Branching and pruning: An optimal temporal POCL planner based on constraint programming.Vincent Vidal & Héctor Geffner - 2006 - Artificial Intelligence 170 (3):298-335.
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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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  • Flexibility and decoupling in Simple Temporal Networks.Michel Wilson, Tomas Klos, Cees Witteveen & Bob Huisman - 2014 - Artificial Intelligence 214 (C):26-44.
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  • Computational complexity of hybrid interval temporal logics.Przemysław Andrzej Wałęga - 2023 - Annals of Pure and Applied Logic 174 (1):103165.
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  • Reasoning about qualitative temporal information.Peter van Beek - 1992 - Artificial Intelligence 58 (1-3):297-326.
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  • Efficient solution techniques for disjunctive temporal reasoning problems.Ioannis Tsamardinos & Martha E. Pollack - 2003 - Artificial Intelligence 151 (1-2):43-89.
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  • A polynomial time algorithm for Zero-Clairvoyant scheduling.K. Subramani - 2007 - Journal of Applied Logic 5 (4):667-680.
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  • From binary temporal relations to non-binary ones and back☆☆Parts of this paper have been published in [36] and in [39]. [REVIEW]Steffen Staab - 2001 - Artificial Intelligence 128 (1-2):1-29.
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  • Acquiring a Self-Model to Enable Autonomous Recovery from Faults and Intrusions.C. M. Kennedy & A. Sloman - 2002 - Journal of Intelligent Systems 12 (1):1-40.
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  • Temporal reasoning about fuzzy intervals.Steven Schockaert & Martine De Cock - 2008 - Artificial Intelligence 172 (8-9):1158-1193.
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  • Processing disjunctions in temporal constraint networks.Eddie Schwalb & Rina Dechter - 1997 - Artificial Intelligence 93 (1-2):29-61.
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  • Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem.Norman Sadeh & Mark S. Fox - 1996 - Artificial Intelligence 86 (1):1-41.
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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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  • Remote Agent: to boldly go where no AI system has gone before.Nicola Muscettola, P. Pandurang Nayak, Barney Pell & Brian C. Williams - 1998 - Artificial Intelligence 103 (1-2):5-47.
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  • On the modelling and optimization of preferences in constraint-based temporal reasoning.Michael D. Moffitt - 2011 - Artificial Intelligence 175 (7-8):1390-1409.
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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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  • A min-flow algorithm for Minimal Critical Set detection in Resource Constrained Project Scheduling.Michele Lombardi & Michela Milano - 2012 - Artificial Intelligence 182-183 (C):58-67.
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  • Planning parallel actions.A. R. Lingard & E. B. Richards - 1998 - Artificial Intelligence 99 (2):261-324.
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  • Effective solution of qualitative interval constraint problems.Peter B. Ladkin & Alexander Reinefeld - 1992 - Artificial Intelligence 57 (1):105-124.
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  • Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results.Philippe Laborie - 2003 - Artificial Intelligence 143 (2):151-188.
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  • Querying temporal and spatial constraint networks in PTIME☆☆A preliminary version of this paper appeared in the proceedings of AAAI-99. A different version of this paper (aimed at a database audience) appeared in the proceedings of the workshop STDBM99. [REVIEW]Manolis Koubarakis & Spiros Skiadopoulos - 2000 - Artificial Intelligence 123 (1-2):223-263.
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  • An Event-Based Fragment of First-Order Logic over Intervals.Savas Konur - 2011 - Journal of Logic, Language and Information 20 (1):49-68.
    We consider a new fragment of first-order logic with two variables. This logic is defined over interval structures. It constitutes unary predicates, a binary predicate and a function symbol. Considering such a fragment of first-order logic is motivated by defining a general framework for event-based interval temporal logics. In this paper, we present a sound, complete and terminating decision procedure for this logic. We show that the logic is decidable, and provide a NEXPTIME complexity bound for satisfiability. This result shows (...)
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  • Computational complexity of linear constraints over the integers.Peter Jonsson & Tomas Lööw - 2013 - Artificial Intelligence 195 (C):44-62.
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  • A unifying approach to temporal constraint reasoning.Peter Jonsson & Christer Bäckström - 1998 - Artificial Intelligence 102 (1):143-155.
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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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  • Deliberation for autonomous robots: A survey.Félix Ingrand & Malik Ghallab - 2017 - Artificial Intelligence 247 (C):10-44.
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  • Temporally and spatially flexible plan execution for dynamic hybrid systems.Andreas G. Hofmann & Brian C. Williams - 2017 - Artificial Intelligence 247 (C):266-294.
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  • Tractable approximations for temporal constraint handling.Robin Hirsch - 2000 - Artificial Intelligence 116 (1-2):287-295.
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  • Step by step – Building representations in algebraic logic.Robin Hirsch & Ian Hodkinson - 1997 - Journal of Symbolic Logic 62 (1):225-279.
    We consider the problem of finding and classifying representations in algebraic logic. This is approached by letting two players build a representation using a game. Homogeneous and universal representations are characterized according to the outcome of certain games. The Lyndon conditions defining representable relation algebras (for the finite case) and a similar schema for cylindric algebras are derived. Finite relation algebras with homogeneous representations are characterized by first order formulas. Equivalence games are defined, and are used to establish whether an (...)
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  • Relation algebras of intervals.Robin Hirsch - 1996 - Artificial Intelligence 83 (2):267-295.
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  • Applying Allen's constraint propagation algorithm for non-linear time.El?Bieta Hajnicz - 1996 - Journal of Logic, Language and Information 5 (2):157-175.
    The famous Allen's interval relations constraint propagation algorithm was intended for linear time. Its 13 primitive relations define all the possible mutual locations of two intervals on the time-axis. In this paper an application of the algorithm for non-linear time is suggested. First, a new primitive relation is added. It is called excludes since an occurrence of one event in a certain course of events excludes an occurrence of the other event in this course. Next, new composition rules for relations (...)
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  • Admissible generalisation of temporal sequences as chronicles.T. Guyet - 2023 - Journal of Applied Non-Classical Logics 33 (3-4):641-653.
    1. Generalising a given set of examples is essential in many machine learning techniques. In principle, a machine learning algorithm builds an abstract model that represents a set of examples. But...
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  • Temporal scenario modelling and recognition based on possibilistic logic.Michel Grabisch - 2003 - Artificial Intelligence 148 (1-2):261-289.
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  • Efficient algorithms for qualitative reasoning about time.Alfonso Gerevini & Lenhart Schubert - 1995 - Artificial Intelligence 74 (2):207-248.
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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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  • An approach to efficient planning with numerical fluents and multi-criteria plan quality.Alfonso E. Gerevini, Alessandro Saetti & Ivan Serina - 2008 - Artificial Intelligence 172 (8-9):899-944.
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  • A probabilistic plan recognition algorithm based on plan tree grammars.Christopher W. Geib & Robert P. Goldman - 2009 - Artificial Intelligence 173 (11):1101-1132.
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  • Fuzzy constraint networks for signal pattern recognition.P. Félix, S. Barro & R. Marín - 2003 - Artificial Intelligence 148 (1-2):103-140.
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  • Arc-consistency for continuous variables.Boi Faltings - 1994 - Artificial Intelligence 65 (2):363-376.
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  • A complete classification of tractability in Allen's algebra relative to subsets of basic relations.Thomas Drakengren & Peter Jonsson - 1998 - Artificial Intelligence 106 (2):205-219.
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  • Planning as constraint satisfaction: Solving the planning graph by compiling it into CSP.Minh Binh Do & Subbarao Kambhampati - 2001 - Artificial Intelligence 132 (2):151-182.
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  • Temporal agent programs.Jürgen Dix, Sarit Kraus & V. S. Subrahmanian - 2001 - Artificial Intelligence 127 (1):87-135.
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  • Solving infinite-domain CSPs using the patchwork property.Konrad K. Dabrowski, Peter Jonsson, Sebastian Ordyniak & George Osipov - 2023 - Artificial Intelligence 317 (C):103880.
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  • The complexity of constraint satisfaction problems for small relation algebras.M. Cristani & R. Hirsch - 2004 - Artificial Intelligence 156 (2):177-196.
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  • Strong temporal planning with uncontrollable durations.Alessandro Cimatti, Minh Do, Andrea Micheli, Marco Roveri & David E. Smith - 2018 - Artificial Intelligence 256 (C):1-34.
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  • An SMT-based approach to weak controllability for disjunctive temporal problems with uncertainty.Alessandro Cimatti, Andrea Micheli & Marco Roveri - 2015 - Artificial Intelligence 224 (C):1-27.
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  • On the computational complexity of querying bounds on differences constraints.Vittorio Brusoni, Luca Console & Paolo Terenziani - 1995 - Artificial Intelligence 74 (2):367-379.
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  • Complexity bounds for the controllability of temporal networks with conditions, disjunctions, and uncertainty.Nikhil Bhargava & Brian C. Williams - 2019 - Artificial Intelligence 271 (C):1-17.
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  • Solving multi-granularity temporal constraint networks.Claudio Bettini, X. Sean Wang & Sushil Jajodia - 2002 - Artificial Intelligence 140 (1-2):107-152.
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