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  1. Width-based search for multi agent privacy-preserving planning.Alfonso E. Gerevini, Nir Lipovetzky, Francesco Percassi, Alessandro Saetti & Ivan Serina - 2023 - Artificial Intelligence 318 (C):103883.
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  • Multi-agent pathfinding with continuous time.Anton Andreychuk, Konstantin Yakovlev, Pavel Surynek, Dor Atzmon & Roni Stern - 2022 - Artificial Intelligence 305 (C):103662.
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  • State space search nogood learning: Online refinement of critical-path dead-end detectors in planning.Marcel Steinmetz & Jörg Hoffmann - 2017 - Artificial Intelligence 245 (C):1-37.
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  • Efficient symbolic search for cost-optimal planning.Álvaro Torralba, Vidal Alcázar, Peter Kissmann & Stefan Edelkamp - 2017 - Artificial Intelligence 242 (C):52-79.
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  • Optimal admissible composition of abstraction heuristics.Michael Katz & Carmel Domshlak - 2010 - Artificial Intelligence 174 (12-13):767-798.
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  • Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners.Alfonso E. Gerevini, Patrik Haslum, Derek Long, Alessandro Saetti & Yannis Dimopoulos - 2009 - Artificial Intelligence 173 (5-6):619-668.
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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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  • Extracting mutual exclusion invariants from lifted temporal planning domains.Sara Bernardini, Fabio Fagnani & David E. Smith - 2018 - Artificial Intelligence 258 (C):1-65.
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  • A heuristic search approach to planning with temporally extended preferences.Jorge A. Baier, Fahiem Bacchus & Sheila A. McIlraith - 2009 - Artificial Intelligence 173 (5-6):593-618.
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  • Long-distance mutual exclusion for planning.Yixin Chen, Ruoyun Huang, Zhao Xing & Weixiong Zhang - 2009 - Artificial Intelligence 173 (2):365-391.
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  • Breadth-first heuristic search.Rong Zhou & Eric A. Hansen - 2006 - Artificial Intelligence 170 (4-5):385-408.
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  • 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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  • Minimax real-time heuristic search.Sven Koenig - 2001 - Artificial Intelligence 129 (1-2):165-197.
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  • Planning as satisfiability: parallel plans and algorithms for plan search.Jussi Rintanen, Keijo Heljanko & Ilkka Niemelä - 2006 - Artificial Intelligence 170 (12-13):1031-1080.
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  • Conformant planning via heuristic forward search: A new approach.Jörg Hoffmann & Ronen I. Brafman - 2006 - Artificial Intelligence 170 (6-7):507-541.
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  • Planning with mental models – Balancing explanations and explicability.Sarath Sreedharan, Tathagata Chakraborti, Christian Muise & Subbarao Kambhampati - 2024 - Artificial Intelligence 335 (C):104181.
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  • Symbolic perimeter abstraction heuristics for cost-optimal planning.Álvaro Torralba, Carlos Linares López & Daniel Borrajo - 2018 - Artificial Intelligence 259 (C):1-31.
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  • The computational complexity of avoiding spurious states in state space abstraction.Sandra Zilles & Robert C. Holte - 2010 - Artificial Intelligence 174 (14):1072-1092.
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  • WeightedA∗search – unifying view and application.Rüdiger Ebendt & Rolf Drechsler - 2009 - Artificial Intelligence 173 (14):1310-1342.
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  • Constraint partitioning in penalty formulations for solving temporal planning problems.Benjamin W. Wah & Yixin Chen - 2006 - Artificial Intelligence 170 (3):187-231.
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  • Multiobjective heuristic state-space planning.Ioannis Refanidis & Ioannis Vlahavas - 2003 - Artificial Intelligence 145 (1-2):1-32.
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  • Generalized planning as heuristic search: A new planning search-space that leverages pointers over objects.Javier Segovia-Aguas, Sergio Jiménez & Anders Jonsson - 2024 - Artificial Intelligence 330 (C):104097.
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  • Conflict-based search for optimal multi-agent pathfinding.Guni Sharon, Roni Stern, Ariel Felner & Nathan R. Sturtevant - 2015 - Artificial Intelligence 219 (C):40-66.
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  • Concise finite-domain representations for PDDL planning tasks.Malte Helmert - 2009 - Artificial Intelligence 173 (5-6):503-535.
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  • Learning from planner performance.Mark Roberts & Adele Howe - 2009 - Artificial Intelligence 173 (5-6):536-561.
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  • Heuristics for planning with penalties and rewards formulated in logic and computed through circuits.Blai Bonet & Héctor Geffner - 2008 - Artificial Intelligence 172 (12-13):1579-1604.
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  • Predicting optimal solution costs with bidirectional stratified sampling in regular search spaces.Levi H. S. Lelis, Roni Stern, Shahab Jabbari Arfaee, Sandra Zilles, Ariel Felner & Robert C. Holte - 2016 - Artificial Intelligence 230 (C):51-73.
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  • Controlling the learning process of real-time heuristic search.Masashi Shimbo & Toru Ishida - 2003 - Artificial Intelligence 146 (1):1-41.
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  • Subdimensional expansion for multirobot path planning.Glenn Wagner & Howie Choset - 2015 - Artificial Intelligence 219 (C):1-24.
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  • Planning as satisfiability: Heuristics.Jussi Rintanen - 2012 - Artificial Intelligence 193 (C):45-86.
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  • Heuristic planning: A declarative approach based on strategies for action selection.Josefina Sierra-Santibáñez - 2004 - Artificial Intelligence 153 (1-2):307-337.
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  • Automatic construction of optimal static sequential portfolios for AI planning and beyond.Sergio Núñez, Daniel Borrajo & Carlos Linares López - 2015 - Artificial Intelligence 226 (C):75-101.
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  • Truncated incremental search.Sandip Aine & Maxim Likhachev - 2016 - Artificial Intelligence 234 (C):49-77.
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  • Conformant plans and beyond: Principles and complexity.Blai Bonet - 2010 - Artificial Intelligence 174 (3-4):245-269.
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  • Robust planning with incomplete domain models.Tuan Nguyen, Sarath Sreedharan & Subbarao Kambhampati - 2017 - Artificial Intelligence 245 (C):134-161.
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  • Anytime search in dynamic graphs.Maxim Likhachev, Dave Ferguson, Geoff Gordon, Anthony Stentz & Sebastian Thrun - 2008 - Artificial Intelligence 172 (14):1613-1643.
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  • Learning heuristic functions for large state spaces.Shahab Jabbari Arfaee, Sandra Zilles & Robert C. Holte - 2011 - Artificial Intelligence 175 (16-17):2075-2098.
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  • CPCES: A planning framework to solve conformant planning problems through a counterexample guided refinement.Alban Grastien & Enrico Scala - 2020 - Artificial Intelligence 284 (C):103271.
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  • Star-topology decoupled state space search.Daniel Gnad & Jörg Hoffmann - 2018 - Artificial Intelligence 257 (C):24-60.
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  • Red–black planning: A new systematic approach to partial delete relaxation.Carmel Domshlak, Jörg Hoffmann & Michael Katz - 2015 - Artificial Intelligence 221 (C):73-114.
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