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Planning with constraints

Artificial Intelligence 16 (2):111-139 (1981)

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  1. A Model of Competence for Counting.Donald A. Smith, James G. Greeno & Theresa M. Vitolo - 1989 - Cognitive Science 13 (2):183-211.
    A theoretical framework Is presented that distinguishes among three knowledge sources that form the basis for generative performance. The three knowledge sources, termed conceptual, procedural, and utilizational competence, were implemented as a computational model that derives plans for counting procedures. In a previous analysis, Greeno, Riley, and Gelman (1984) developed a characterization of the conceptual competence (implicit understanding of general concepts and principles) associated with the skill of counting and related conceptual competence to various models of performance. In the current (...)
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  • The organization of expert systems, a tutorial.Mark Stefik, Jan Aikins, Robert Balzer, John Benoit, Lawrence Birnbaum, Frederick Hayes-Roth & Earl Sacerdoti - 1982 - Artificial Intelligence 18 (2):135-173.
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  • Heuristic classification.William J. Clancey - 1985 - Artificial Intelligence 27 (3):289-350.
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  • Program Structure and Design.Robert S. Rist - 1995 - Cognitive Science 19 (4):507-562.
    Most models of computer programming explain the programmer's behaviour by a single design strategy. This article presents a cognitive architecture that uses cue‐based search to model multiple design strategies including procedural, functional, means‐end or focal, and opportunistic design. The model has been implemented in an artificial intelligence (AI) system that generates Pascal programs from English specifications.Knowledge is represented as nodes that reside in internal or external memory, where a node encodes an action that may range from a line of code (...)
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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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  • 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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  • HTN planning: Overview, comparison, and beyond.Ilche Georgievski & Marco Aiello - 2015 - Artificial Intelligence 222:124-156.
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  • The epistemology of a rule-based expert system —a framework for explanation.William J. Clancey - 1983 - Artificial Intelligence 20 (3):215-251.
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  • The use of design descriptions in automated diagnosis.Michael R. Genesereth - 1984 - Artificial Intelligence 24 (1-3):411-436.
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  • Temporal data base management.Thomas L. Dean & Drew V. McDermott - 1987 - Artificial Intelligence 32 (1):1-55.
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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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  • Planning with constraints.Mark Stefik - 1981 - Artificial Intelligence 16 (2):111-139.
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  • EVEXED and MEET for mechanical design: testing structural decomposition and constraint propagation.L. Steinberg & N. Langrana - 1996 - Artificial Intelligence 84 (1-2):37-56.
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  • Arc consistency: parallelism and domain dependence.Paul R. Cooper & Michael J. Swain - 1992 - Artificial Intelligence 58 (1-3):207-235.
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  • Partial-order planning.Anthony Barrett & Daniel S. Weld - 1994 - Artificial Intelligence 67 (1):71-112.
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  • Automatically generating abstractions for planning.Craig A. Knoblock - 1994 - Artificial Intelligence 68 (2):243-302.
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  • Understanding planner behavior.Adele E. Howe & Paul R. Cohen - 1995 - Artificial Intelligence 76 (1-2):125-166.
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  • Domain-independent planning Representation and plan generation.David E. Wilkins - 1984 - Artificial Intelligence 22 (3):269-301.
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  • O-Plan: The open planning architecture.Ken Currie & Austin Tate - 1991 - Artificial Intelligence 52 (1):49-86.
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  • Planning and meta-planning.Mark Stefik - 1981 - Artificial Intelligence 16 (2):141-169.
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  • Planning for conjunctive goals.David Chapman - 1987 - Artificial Intelligence 32 (3):333-377.
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  • Constraint propagation with interval labels.Ernest Davis - 1987 - Artificial Intelligence 32 (3):281-331.
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  • Downward refinement and the efficiency of hierarchical problem solving.Fahiem Bacchus & Qiang Yang - 1994 - Artificial Intelligence 71 (1):43-100.
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  • Multiple perspective dynamic decision making.Tze Yun Leong - 1998 - Artificial Intelligence 105 (1-2):209-261.
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  • A theory of conflict resolution in planning.Qiang Yang - 1992 - Artificial Intelligence 58 (1-3):361-392.
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  • Planning and understanding: A computational approach to human reasoning.Daniel M. Russell - 1984 - Artificial Intelligence 23 (2):239-242.
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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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  • 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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  • Model construction operators.William J. Clancey - 1992 - Artificial Intelligence 53 (1):1-115.
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