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  1. World graphs: A partial model of spatial behavior.Israel Lieblich & Michael A. Arbib - 1982 - Behavioral and Brain Sciences 5 (4):651-659.
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  • Ecologizing world graphs.Robert E. Shaw & Ennio Mingolla - 1982 - Behavioral and Brain Sciences 5 (4):648-650.
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  • Gems set into a base matrix.Rudolf Jander - 1981 - Behavioral and Brain Sciences 4 (4):627-628.
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  • Prototypes, Location, and Associative Networks (PLAN): Towards a Unified Theory of Cognitive Mapping.Eric Chown, Stephen Kaplan & David Kortenkamp - 1995 - Cognitive Science 19 (1):1-51.
    An integrated representation of large‐scale space, or cognitive map, colled PLAN, is presented that attempts to address a broader spectrum of issues than has been previously attempted in a single model. Rather than examining way‐finding as a process separate from the rest of cognition, one or the fundamental goals of this work is to examine how the wayfinding process is integrated into general cognition. One result of this approach is that the model is “heads‐up,” or scene‐based, because it takes advantage (...)
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  • Afordancje dla robotów: krótki przegląd.Robert St Amant, Arpan Chakraborty & Thomas E. Horton - 2014 - Avant: Trends in Interdisciplinary Studies 5 (1):133-150.
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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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  • 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 Hybrid of Search Efficiency Mechanisms: Pruning Learning Heuristic Hybrid.Reza Zamani - 2005 - Journal of Intelligent Systems 14 (4):265-288.
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  • Maps, space, and places.Roger M. Downs - 1982 - Behavioral and Brain Sciences 5 (4):641-642.
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  • Multiple representations of space underlying behavior.Israel Lieblich & Michael A. Arbib - 1982 - Behavioral and Brain Sciences 5 (4):627-640.
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  • Précis of Gallistel's The organization of action: A new synthesis.C. R. Gallistel - 1981 - Behavioral and Brain Sciences 4 (4):609-619.
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  • A new cognitive model of long-term memory for intentions.Thor Grünbaum, Franziska Oren & Søren Kyllingsbæk - 2021 - Cognition 215 (C):104817.
    In this paper, we propose a new mathematical model of retrieval of intentions from long-term memory. We model retrieval as a stochastic race between a plurality of potentially relevant intentions stored in long-term memory. Psychological theories are dominated by two opposing conceptions of the role of memory in temporally extended agency – as when a person has to remember to make a phone call in the afternoon because, in the morning, she promised she would do so. According to the Working (...)
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  • Analyzing intention in utterances.James F. Allen & C. Raymond Perrault - 1980 - Artificial Intelligence 15 (3):143-178.
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  • Theorem proving with abstraction.David A. Plaisted - 1981 - Artificial Intelligence 16 (1):47-108.
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  • Strategies in heuristic search.M. P. Georgeff - 1983 - Artificial Intelligence 20 (4):393-425.
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  • Reasoning about model accuracy.Daniel S. Weld - 1992 - Artificial Intelligence 56 (2-3):255-300.
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  • Abstraction and approximate decision-theoretic planning.Richard Dearden & Craig Boutilier - 1997 - Artificial Intelligence 89 (1-2):219-283.
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  • A direct manipulation interface for a user enhanceable crowd simulator.Len Bottaci - 1995 - Journal of Intelligent Systems 5 (2-4):249-272.
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  • Oscillators in human motor systems.Brian Craske - 1981 - Behavioral and Brain Sciences 4 (4):621-622.
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  • Network foci in integrated action: Units or something else?John C. Fentress - 1981 - Behavioral and Brain Sciences 4 (4):623-624.
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  • Representing and applying knowledge for argumentation in a social context.Chris Reed - 1997 - AI and Society 11 (1-2):138-154.
    The concept of argumentation in AI is based almost exclusively on the use of formal, abstract representations. Despite their appealing computational properties, these abstractions become increasingly divorced from their real world counterparts, and, crucially, lose the ability to express the rich gamut of natural argument forms required for creating effective text. In this paper, the demands that socially situated argumentation places on knowledge representation are explored, and the various problems with existing formalisations are discussed. Insights from argumentation theory and social (...)
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  • (1 other version)Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry.Kenneth R. Koedinger & John R. Anderson - 1990 - Cognitive Science 14 (4):511-550.
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  • CHIRON: Planning in an open-textured domain. [REVIEW]Kathryn E. Sanders - 2001 - Artificial Intelligence and Law 9 (4):225-269.
    Planning problems arise in law when an individual (or corporation)wants to perform a sequence of actions that raises legal issues. Manylawyers make their living planning transactions, and a system thathelped them to solve these problems would be in demand.The designer of such a system in a common-law domain must addressseveral difficult issues, including the open-textured nature of legal rules,the relationship between legal rules and cases, the adversarial nature ofthe domain, and the role of argument. In addition, the system's design isconstrained (...)
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  • Planning with constraints.Mark Stefik - 1981 - Artificial Intelligence 16 (2):111-139.
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  • Causal approximations.P. Pandurang Nayak - 1994 - Artificial Intelligence 70 (1-2):277-334.
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  • Complexity, decidability and undecidability results for domain-independent planning.Kutluhan Erol, Dana S. Nau & V. S. Subrahmanian - 1995 - Artificial Intelligence 76 (1-2):75-88.
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  • Behavior ignored.Peter C. Reynolds - 1981 - Behavioral and Brain Sciences 4 (4):637-637.
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  • Gallistel's metatheory of action.H. L. Roitblat - 1981 - Behavioral and Brain Sciences 4 (4):637-638.
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  • A new synthesis?Michael A. Arbib - 1981 - Behavioral and Brain Sciences 4 (4):619-619.
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  • Independence and interaction in behavioral units.William Chapple - 1981 - Behavioral and Brain Sciences 4 (4):620-621.
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  • Conversation as Planned Behavior.Jerry R. Hobbs & David Andreoff Evans - 1980 - Cognitive Science 4 (4):349-377.
    In this paper, planning models developed in artificial intelligence are applied to the kind of planning that must be carried out by participants in a conversation. A planning mechanism is defined, and a short fragment of a free‐flowing videotaped conversation is described. The bulk of the paper is then devoted to an attempt to understand the conversation in terms of the planning mechanism. This microanalysis suggests ways in which the planning mechanism must be augmented, and reveals several important conversational phenomena (...)
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  • Constraints—A language for expressing almost-hierarchical descriptions.Gerald Jay Sussman & Guy Lewis Steele - 1980 - Artificial Intelligence 14 (1):1-39.
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  • Analysis of adaptation and environment.Ian Horswill - 1995 - Artificial Intelligence 73 (1-2):1-30.
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  • Metóda, problém a úloha (Method, Problem and Task).František Gahér & Vladimir Marko - 2017 - Bratislava: Univerzita Komenského.
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  • Looking for nodes and edges.Arnold Trehub - 1982 - Behavioral and Brain Sciences 5 (4):650-651.
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  • Dynamic servomechanisms are more fun: A critical look at chapters 6 and 7 of The organization of action.E. R. Lewis - 1981 - Behavioral and Brain Sciences 4 (4):629-630.
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  • Meta‐Planning: Representing and Using Knowledge About Planning in Problem Solving and Natural Language Understanding.Robert Wilensky - 1981 - Cognitive Science 5 (3):197-233.
    This paper is concerned with those elements of planning knowledge that are common to both understanding someone else's plan and creating a plan for one's own use. This planning knowledge can be divided into two bodies: Knowledge about the world, and knowledge about the planning process itself. Our interest here is primarily with the latter corpus. The central thesis is that much of the knowledge about the planning process itself can be formulated in terms of higher‐level goals and plans called (...)
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  • Using patterns and plans in chess.David Wilkins - 1980 - Artificial Intelligence 14 (2):165-203.
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  • Planning as search: A quantitative approach.Richard E. Korf - 1987 - Artificial Intelligence 33 (1):65-88.
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  • Probabilistic logic revisited.Nils J. Nilsson - 1993 - Artificial Intelligence 59 (1-2):39-42.
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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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  • Calculating criticalities.A. Bundy, F. Giunchiglia, R. Sebastiani & T. Walsh - 1996 - Artificial Intelligence 88 (1-2):39-67.
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  • (1 other version)Speeding up problem solving by abstraction: a graph oriented approach.R. C. Holte, T. Mkadmi, R. M. Zimmer & A. J. MacDonald - 1996 - Artificial Intelligence 85 (1-2):321-361.
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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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  • The cognitive map must be a separate module.Benjamin Kuipers - 1982 - Behavioral and Brain Sciences 5 (4):645-646.
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  • Where's the action?N. J. Mackintosh - 1981 - Behavioral and Brain Sciences 4 (4):631-631.
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  • Krister Segerberg on Logic of Actions.Robert Trypuz (ed.) - 2013 - Dordrecht, Netherland: Springer Verlag.
    Belief revision from the point of view of doxastic logic. Logic Journal of the IGPL, 3(4), 535–553. Segerberg, K. (1995). Conditional action. In G. Crocco, L. Fariñas, & A. Herzig (Eds.), Conditionals: From philosophy to computer science, Studies ...
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  • Dynamic backward reasoning systems.Antoni Ligęza - 1990 - Artificial Intelligence 43 (2):127-152.
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  • Knowledge-based proof planning.Erica Melis & Jörg Siekmann - 1999 - Artificial Intelligence 115 (1):65-105.
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  • The special nature of spatial information.Michael Potegal - 1982 - Behavioral and Brain Sciences 5 (4):647-648.
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