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  1. State-set branching: Leveraging BDDs for heuristic search.Rune M. Jensen, Manuela M. Veloso & Randal E. Bryant - 2008 - Artificial Intelligence 172 (2-3):103-139.
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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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  • Fast planning through planning graph analysis.Avrim L. Blum & Merrick L. Furst - 1997 - Artificial Intelligence 90 (1-2):281-300.
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  • Engineering and compiling planning domain models to promote validity and efficiency.T. L. McCluskey & J. M. Porteous - 1997 - Artificial Intelligence 95 (1):1-65.
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  • Automatically selecting and using primary effects in planning: theory and experiments.Eugene Fink & Qiang Yang - 1997 - Artificial Intelligence 89 (1-2):285-315.
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  • Planning as refinement search: a unified framework for evaluating design tradeoffs in partial-order planning.Subbarao Kambhampati, Craig A. Knoblock & Qiang Yang - 1995 - Artificial Intelligence 76 (1-2):167-238.
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  • Using temporal logics to express search control knowledge for planning.Fahiem Bacchus & Froduald Kabanza - 2000 - Artificial Intelligence 116 (1-2):123-191.
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  • The logical foundations of goal-regression planning in autonomous agents.John L. Pollock - 1998 - Artificial Intelligence 106 (2):267-334.
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  • State-variable planning under structural restrictions: algorithms and complexity.Peter Jonsson & Christer Bäckström - 1998 - Artificial Intelligence 100 (1-2):125-176.
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  • Towards efficient universal planning: A randomized approach.Peter Jonsson, Patrik Haslum & Christer Bäckström - 2000 - Artificial Intelligence 117 (1):1-29.
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  • The Little Nell Problem: reasonable and resolute maintenance of agent intentions.Richmond H. Thomason - 2018 - Synthese 195 (1):433-440.
    The Little Nell Problem was formulated by Drew McDermott in 1982. It reveals unexpected complexities in the interaction of the beliefs and intentions of a planning agent. This paper discusses the problem and proposes a solution.
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