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  1. 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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  • Reasoning about partially ordered events.Thomas Dean & Mark Boddy - 1988 - Artificial Intelligence 36 (3):375-399.
  • The scope and limits of simulation in automated reasoning.Ernest Davis & Gary Marcus - 2016 - Artificial Intelligence 233 (C):60-72.
  • Knowledge and communication: A first-order theory.Ernest Davis - 2005 - Artificial Intelligence 166 (1-2):81-139.
  • How does a box work? A study in the qualitative dynamics of solid objects.Ernest Davis - 2011 - Artificial Intelligence 175 (1):299-345.
  • Time and modality in a natural language interface to a planning system.R. S. Crouch & S. G. Pulman - 1993 - Artificial Intelligence 63 (1-2):265-304.
  • The computational complexity of propositional STRIPS planning.Tom Bylander - 1994 - Artificial Intelligence 69 (1-2):165-204.
  • A probabilistic analysis of prepositional STRIPS planning.Tom Bylander - 1996 - Artificial Intelligence 81 (1-2):241-271.
  • Calculating criticalities.A. Bundy, F. Giunchiglia, R. Sebastiani & T. Walsh - 1996 - Artificial Intelligence 88 (1-2):39-67.
  • Stochastic dynamic programming with factored representations.Craig Boutilier, Richard Dearden & Moisés Goldszmidt - 2000 - Artificial Intelligence 121 (1-2):49-107.
  • Fast planning through planning graph analysis.Avrim L. Blum & Merrick L. Furst - 1997 - Artificial Intelligence 90 (1-2):281-300.
  • Expressive equivalence of planning formalisms.Christer Bäckström - 1995 - Artificial Intelligence 76 (1-2):17-34.
  • Partial-order planning.Anthony Barrett & Daniel S. Weld - 1994 - Artificial Intelligence 67 (1):71-112.
  • Plans and situated actions: The problem of human-machine communication.Philip E. Agre - 1990 - Artificial Intelligence 43 (3):369-384.
  • Computational research on interaction and agency.Philip E. Agre - 1995 - Artificial Intelligence 72 (1-2):1-52.
  • A theory of conflict resolution in planning.Qiang Yang - 1992 - Artificial Intelligence 58 (1-3):361-392.
  • Representations of commonsense knowledge.Daniel S. Weld - 1993 - Artificial Intelligence 61 (1):113-120.
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  • Plan-based integration of natural language and graphics generation.Wolfgang Wahlster, Elisabeth André, Wolfgang Finkler, Hans-Jürgen Profitlich & Thomas Rist - 1993 - Artificial Intelligence 63 (1-2):387-427.
  • Reasoning agents in a dynamic world: The frame problem.Jozsef A. Toth - 1995 - Artificial Intelligence 73 (1-2):323-369.
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  • The roles of associational and causal reasoning in problem solving.Reid G. Simmons - 1992 - Artificial Intelligence 53 (2-3):159-207.
  • Processes and continuous change in a SAT-based planner.Ji-Ae Shin & Ernest Davis - 2005 - Artificial Intelligence 166 (1-2):194-253.
  • Kernel functions for case-based planning.Ivan Serina - 2010 - Artificial Intelligence 174 (16-17):1369-1406.
  • The use of dynamics in an intelligent controller for a space faring rescue robot.Marcel Schoppers - 1995 - Artificial Intelligence 73 (1-2):175-230.
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  • A preliminary analysis of the Soar architecture as a basis for general intelligence.Paul S. Rosenbloom, John E. Laird, Allen Newell & Robert McCarl - 1991 - Artificial Intelligence 47 (1-3):289-325.
  • The logical foundations of goal-regression planning in autonomous agents.John L. Pollock - 1998 - Artificial Intelligence 106 (2):267-334.
  • On the computational complexity of temporal projection, planning, and plan validation.Bernhard Nebel & Christer Bäckström - 1994 - Artificial Intelligence 66 (1):125-160.
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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.
  • World modeling for the dynamic construction of real-time control plans.David J. Musliner, Edmund H. Durfee & Kang G. Shin - 1995 - Artificial Intelligence 74 (1):83-127.
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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.
  • 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.
  • 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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  • Artificial Intelligence and Law: How to Get There from Here.L. Thorne Mccarty - 1990 - Ratio Juris 3 (2):189-200.
    . This paper offers a survey of the current state of Artificial Intelligence and Law, and makes recommendations for future research. Two main areas of investigation are discussed: the practical work on intelligent legal information systems, and the theoretical work on computational models of legal reasoning. In both areas, the knowledge representation problem is identified as the most important issue facing this field.
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  • On the evaluation of agent behaviors.Amol Dattatraya Mali - 2003 - Artificial Intelligence 143 (1):1-17.
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  • On the undecidability of probabilistic planning and related stochastic optimization problems.Omid Madani, Steve Hanks & Anne Condon - 2003 - Artificial Intelligence 147 (1-2):5-34.
  • Vision, instruction, and action.Damian M. Lyons - 1995 - Artificial Intelligence 73 (1-2):387-401.
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  • Planning parallel actions.A. R. Lingard & E. B. Richards - 1998 - Artificial Intelligence 99 (2):261-324.
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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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  • Multiple perspective dynamic decision making.Tze Yun Leong - 1998 - Artificial Intelligence 105 (1-2):209-261.
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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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  • Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results.Philippe Laborie - 2003 - Artificial Intelligence 143 (2):151-188.
  • An algorithm for probabilistic planning.Nicholas Kushmerick, Steve Hanks & Daniel S. Weld - 1995 - Artificial Intelligence 76 (1-2):239-286.
  • 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.
  • On the nature and role of modal truth criteria in planning.Subbarao Kambhampati & Dana S. Nau - 1996 - Artificial Intelligence 82 (1-2):129-155.
  • Multi-contributor causal structures for planning: a formalization and evaluation.Subbarao Kambhampati - 1994 - Artificial Intelligence 69 (1-2):235-278.
  • A validation-structure-based theory of plan modification and reuse.Subbarao Kambhampati & James A. Hendler - 1992 - Artificial Intelligence 55 (2-3):193-258.
  • 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.
  • A theoretical analysis of conjunctive-goal problems.David Joslin & John Roach - 1989 - Artificial Intelligence 41 (1):97-106.
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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.
  • Coordinating Agents in Design: A Formal Analysis.U. V. Johar - 1998 - Journal of Intelligent Systems 8 (3-4):291-314.
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  • Embodiment versus memetics.Joanna J. Bryson - 2007 - Mind and Society 7 (1):77-94.
    The term embodiment identifies a theory that meaning and semantics cannot be captured by abstract, logical systems, but are dependent on an agent’s experience derived from being situated in an environment. This theory has recently received a great deal of support in the cognitive science literature and is having significant impact in artificial intelligence. Memetics refers to the theory that knowledge and ideas can evolve more or less independently of their human-agent substrates. While humans provide the medium for this evolution, (...)
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