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  1. Explicatures are NOT Cancellable.Alessandro Capone - 2013 - In Perspectives on Linguistic Pragmatics. Cham: Springer. pp. 131-151.
    Explicatures are not cancellable. Theoretical considerations.
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  • The computational complexity of propositional STRIPS planning.Tom Bylander - 1994 - Artificial Intelligence 69 (1-2):165-204.
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  • A probabilistic analysis of prepositional STRIPS planning.Tom Bylander - 1996 - Artificial Intelligence 81 (1-2):241-271.
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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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  • Stochastic dynamic programming with factored representations.Craig Boutilier, Richard Dearden & Moisés Goldszmidt - 2000 - Artificial Intelligence 121 (1-2):49-107.
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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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  • Expressive equivalence of planning formalisms.Christer Bäckström - 1995 - Artificial Intelligence 76 (1-2):17-34.
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  • Partial-order planning.Anthony Barrett & Daniel S. Weld - 1994 - Artificial Intelligence 67 (1):71-112.
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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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  • Computational research on interaction and agency.Philip E. Agre - 1995 - Artificial Intelligence 72 (1-2):1-52.
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  • Kernel functions for case-based planning.Ivan Serina - 2010 - Artificial Intelligence 174 (16-17):1369-1406.
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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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  • A validation-structure-based theory of plan modification and reuse.Subbarao Kambhampati & James A. Hendler - 1992 - Artificial Intelligence 55 (2-3):193-258.
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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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  • Incrementality and Intention-Recognition in Utterance Processing.Eleni Gregoromichelaki, Ruth Kempson, Matthew Purver, Gregory Mills, Ronnie Cann, Wilfried Meyer-Viol & Patrick G. T. Healey - 2011 - Dialogue and Discourse 2 (1):199-232.
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  • A geometric approach to error detection and recovery for robot motion planning with uncertainty.Bruce R. Donald - 1988 - Artificial Intelligence 37 (1-3):223-271.
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  • 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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  • Knowledge and communication: A first-order theory.Ernest Davis - 2005 - Artificial Intelligence 166 (1-2):81-139.
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  • 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.
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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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  • 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.
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  • 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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  • 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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  • Between epistemology and hermeneutics.Dimitri Ginev - 1995 - Science & Education 4 (2):147-159.
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  • Vision, instruction, and action.Damian M. Lyons - 1995 - Artificial Intelligence 73 (1-2):387-401.
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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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  • (1 other version)The logical foundations of goal-regression planning in autonomous agents.John L. Pollock - 1998 - Artificial Intelligence 106 (2):267-334.
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  • The influence of k-dependence on the complexity of planning.Omer Giménez & Anders Jonsson - 2012 - Artificial Intelligence 177-179 (C):25-45.
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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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  • An algorithm for probabilistic planning.Nicholas Kushmerick, Steve Hanks & Daniel S. Weld - 1995 - Artificial Intelligence 76 (1-2):239-286.
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  • Reasoning about partially ordered events.Thomas Dean & Mark Boddy - 1988 - Artificial Intelligence 36 (3):375-399.
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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 information invariants in robotics.Bruce Randall Donald - 1995 - Artificial Intelligence 72 (1-2):217-304.
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  • Coordinating Agents in Design: A Formal Analysis.U. V. Johar - 1998 - Journal of Intelligent Systems 8 (3-4):291-314.
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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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  • Embedding decision-analytic control in a learning architecture.Oren Etzioni - 1991 - Artificial Intelligence 49 (1-3):129-159.
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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.
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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.
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  • 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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  • Representations of commonsense knowledge.Daniel S. Weld - 1993 - Artificial Intelligence 61 (1):113-120.
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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.
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  • Deriving consensus in multiagent systems.Eithan Ephrati & Jeffrey S. Rosenschein - 1996 - Artificial Intelligence 87 (1-2):21-74.
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  • Approximate planning.Matthew L. Ginsberg - 1995 - Artificial Intelligence 76 (1-2):89-123.
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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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  • Multiple perspective dynamic decision making.Tze Yun Leong - 1998 - Artificial Intelligence 105 (1-2):209-261.
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  • Acquiring search-control knowledge via static analysis.Oren Etzioni - 1993 - Artificial Intelligence 62 (2):255-301.
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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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  • Plan reuse versus plan generation: a theoretical and empirical analysis.Bernhard Nebel & Jana Koehler - 1995 - Artificial Intelligence 76 (1-2):427-454.
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  • Theory and algorithms for plan merging.David E. Foulser, Ming Li & Qiang Yang - 1992 - Artificial Intelligence 57 (2-3):143-181.
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