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  1. Theory and algorithms for plan merging.David E. Foulser, Ming Li & Qiang Yang - 1992 - Artificial Intelligence 57 (2-3):143-181.
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  • A unified framework for explanation-based generalization of partially ordered and partially instantiated plans.Subbarao Kambhampati & Smadar Kedar - 1994 - Artificial Intelligence 67 (1):29-70.
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  • Planning: What it is, what it could be, an introduction to the special issue on planning and scheduling.Drew McDermott & James Hendler - 1995 - Artificial Intelligence 76 (1-2):1-16.
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  • A general programming language for unified planning and control.Richard Levinson - 1995 - Artificial Intelligence 76 (1-2):319-375.
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  • Planning from second principles.Jana Koehler - 1996 - Artificial Intelligence 87 (1-2):145-186.
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  • A probabilistic analysis of prepositional STRIPS planning.Tom Bylander - 1996 - Artificial Intelligence 81 (1-2):241-271.
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  • Kernel functions for case-based planning.Ivan Serina - 2010 - Artificial Intelligence 174 (16-17):1369-1406.
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  • Partial-order planning.Anthony Barrett & Daniel S. Weld - 1994 - Artificial Intelligence 67 (1):71-112.
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  • Plan reuse versus plan generation: a theoretical and empirical analysis.Bernhard Nebel & Jana Koehler - 1995 - Artificial Intelligence 76 (1-2):427-454.
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  • Permissive planning: extending classical planning to uncertain task domains.Gerald F. DeJong & Scott W. Bennett - 1997 - Artificial Intelligence 89 (1-2):173-217.
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  • Convention in joint activity.Richard Alterman & Andrew Garland - 2001 - Cognitive Science 25 (4):611-657.
    Conventional behaviors develop from practice for regularly occurring problems of coordination within a community of actors. Reusing and extending conventional methods for coordinating behavior is the task of everyday reasoning.The computational model presented in the paper details the emergence of convention in circumstances where there is no ruling body of knowledge developed by prior generations of actors within the community to guide behavior. The framework we assume combines social theories of cognition with human information processing models that have been developed (...)
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  • CABINS: a framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair.Kazuo Miyashita & Katia Sycara - 1995 - Artificial Intelligence 76 (1-2):377-426.
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  • Multi-contributor causal structures for planning: a formalization and evaluation.Subbarao Kambhampati - 1994 - Artificial Intelligence 69 (1-2):235-278.
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  • The uses of plans.Martha E. Pollack - 1992 - Artificial Intelligence 57 (1):43-68.
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  • Adaptation-guided retrieval: questioning the similarity assumption in reasoning.Barry Smyth & Mark T. Keane - 1998 - Artificial Intelligence 102 (2):249-293.
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  • Model-lite planning: Case-based vs. model-based approaches.Hankz Hankui Zhuo & Subbarao Kambhampati - 2017 - Artificial Intelligence 246 (C):1-21.
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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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  • Localized planning with action-based constraints.Amy L. Lansky - 1998 - Artificial Intelligence 98 (1-2):49-136.
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  • Lifelong Planning A∗.Sven Koenig, Maxim Likhachev & David Furcy - 2004 - Artificial Intelligence 155 (1-2):93-146.
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