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Planning and meta-planning

Artificial Intelligence 16 (2):141-169 (1981)

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  1. Planning for conjunctive goals.David Chapman - 1987 - Artificial Intelligence 32 (3):333-377.
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  • Connectionist Models and Their Properties.J. A. Feldman & D. H. Ballard - 1982 - Cognitive Science 6 (3):205-254.
    Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...)
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  • The uses of plans.Martha E. Pollack - 1992 - Artificial Intelligence 57 (1):43-68.
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  • SALT: A knowledge acquisition language for propose-and-revise systems.Sandra Marcus & John McDermott - 1989 - Artificial Intelligence 39 (1):1-37.
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  • Explaining and repairing plans that fail.Kristian J. Hammond - 1990 - Artificial Intelligence 45 (1-2):173-228.
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  • A hierarchy machine: Learning to optimize from nature and humans.Martin Pelikan & David E. Goldberg - 2003 - Complexity 8 (5):36-45.
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  • A metatheory of a mechanized object theory.Fausto Giunchiglia & Paolo Traverso - 1996 - Artificial Intelligence 80 (2):197-241.
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  • Partial and total‐order planning: evidence from normal and prefrontally damaged populations.Mary Jo Rattermann, Lee Spector, Jordan Grafman, Harvey Levin & Harriet Harward - 2001 - Cognitive Science 25 (6):941-975.
    This paper examines human planning abilities, using as its inspiration planning techniques developed in artificial intelligence. AI research has shown that in certain problems partial‐order planners, which manipulate partial plans while not committing to a particular ordering of those partial plans, are more efficient than total‐order planners, which represent all partial plans as totally ordered. This research asks whether total‐order planning and/or partial‐order planning are accurate descriptions of human planning, and if different populations use different planning techniques. Using a simple (...)
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  • Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real‐World Problem‐Solving Task.Ashwin Ram, S. Narayanan & Michael T. Cox - 1995 - Cognitive Science 19 (3):289-340.
    This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real-world troubleshooting tasks. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem-solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which (...)
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  • Plans and situated actions: The problem of human-machine communication.Philip E. Agre - 1990 - Artificial Intelligence 43 (3):369-384.
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  • Problem‐Solving Restructuration: Elimination of Implicit Constraints.Jean-François Richard, Sébastien Poitrenaud & Charles Tijus - 1993 - Cognitive Science 17 (4):497-529.
    A general model of problem‐solving processes based on misconception elimination is presented to simulate both impasses and solving processes. The model operates on goal‐related rules and a set of constraint rules in the form of “if (state or goal), do not (Action)” for the explicit constraints in the instructions and the implicit constraints that come from misconceptions of legal moves. When impasses occur, a constraint elimination mechanism is applied. Because successive eliminations of implicit constraints enlarge the problem space and have (...)
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  • Planning with constraints.Mark Stefik - 1981 - Artificial Intelligence 16 (2):111-139.
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  • A blackboard architecture for control.Barbara Hayes-Roth - 1985 - Artificial Intelligence 26 (3):251-321.
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  • Making compromises among antagonist constraints in a planner.Yannick Descotte & Jean-Claude Latombe - 1985 - Artificial Intelligence 27 (2):183-217.
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  • The logical structure of classical genetics.Wolfgang Balzer & Pablo Lorenzano - 2000 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 31 (2):243-266.
    We present a reconstruction of so-called classical, formal or Mendelian genetics using a notation which we believe is more legible than that of earlier accounts, and lends itself easily to computer implementation, for instance in PROLOG. By drawing from, and emending, earlier work of Balzer and Dawe (1986,1997), the present account presents the three most important lines of development of classical genetics: the so-called Mendel's laws, linkage genetics and gene mapping, in the form of a theory-net. This shows that the (...)
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