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  1. John McCarthy's legacy.Leora Morgenstern & Sheila A. McIlraith - 2011 - Artificial Intelligence 175 (1):1-24.
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  • The imagination: Cognitive, pre-cognitive, and meta-cognitive aspects.Kieron P. O’Connor & Frederick Aardema - 2005 - Consciousness and Cognition 14 (2):233-256.
    This article is an attempt to situate imagination within consciousness complete with its own pre-cognitive, cognitive, and meta-cognitive domains. In the first sections we briefly review traditional philosophical and psychological conceptions of the imagination. The majority have viewed perception and imagination as separate faculties, performing distinct functions. A return to a phenomenological account of the imagination suggests that divisions between perception and imagination are transcended by precognitive factors of sense of reality and non-reality where perception and imagination play an indivisible (...)
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  • A logic for reasoning with inconsistent knowledge.Nico Roos - 1992 - Artificial Intelligence 57 (1):69-103.
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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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  • Nonmonotonic reasoning in the framework of situation calculus.Andrew B. Baker - 1991 - Artificial Intelligence 49 (1-3):5-23.
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  • Changes of disjunctively closed bases.Sven Ove Hansson - 1993 - Journal of Logic, Language and Information 2 (4):255-284.
    An operator of contraction for a belief set (a theory) can be obtained by assigning to it a belief base and an operator of partial meet contraction for that base. It is argued that closure of the base under disjunction is an intuitively reasonable condition. Axiomatic characterizations are given of the contractions of belief sets that can be generated by (various types of) partial meet contraction on disjunctively closed bases. The corresponding revision operators are also characterized. Finally, some results are (...)
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  • Integrating actions and state constraints: A closed-form solution to the ramification problem.Sheila A. McIlraith - 2000 - Artificial Intelligence 116 (1-2):87-121.
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  • A unifying action calculus.Michael Thielscher - 2011 - Artificial Intelligence 175 (1):120-141.
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  • Ramification and causality.Michael Thielscher - 1997 - Artificial Intelligence 89 (1-2):317-364.
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  • Belief Revision, Conditional Logic and Nonmonotonic Reasoning.Wayne Wobcke - 1995 - Notre Dame Journal of Formal Logic 36 (1):55-103.
    We consider the connections between belief revision, conditional logic and nonmonotonic reasoning, using as a foundation the approach to theory change developed by Alchourrón, Gärdenfors and Makinson (the AGM approach). This is first generalized to allow the iteration of theory change operations to capture the dynamics of epistemic states according to a principle of minimal change of entrenchment. The iterative operations of expansion, contraction and revision are characterized both by a set of postulates and by Grove's construction based on total (...)
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  • On the complexity of propositional knowledge base revision, updates, and counterfactuals.Thomas Eiter & Georg Gottlob - 1992 - Artificial Intelligence 57 (2-3):227-270.
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  • Constructive Modelings for Theory Change.Pavlos Peppas & Mary-Anne Williams - 1995 - Notre Dame Journal of Formal Logic 36 (1):120-133.
    Alchourrón, Gärdenfors and Makinson have developed and investigated a set of rationality postulates which appear to capture much of what is required of any rational system of theory revision. This set of postulates describes a class of revision functions, however it does not provide a constructive way of defining such a function. There are two principal constructions of revision functions, namely an epistemic entrenchment and a system of spheres. We refer to their approach as the AGM paradigm. We provide a (...)
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  • Reasoning about actions: steady versus stabilizing state constraints.Michael Thielscher - 1998 - Artificial Intelligence 104 (1-2):339-355.
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  • Chronological ignorance: Experiments in nonmonotonic temporal reasoning.Yoav Shoham - 1988 - Artificial Intelligence 36 (3):279-331.
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  • The Qualification Problem: A solution to the problem of anomalous models.Michael Thielscher - 2001 - Artificial Intelligence 131 (1-2):1-37.
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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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  • An Information-Based Theory of Conditionals.Wayne Wobcke - 2000 - Notre Dame Journal of Formal Logic 41 (2):95-141.
    We present an approach to combining three areas of research which we claim are all based on information theory: knowledge representation in Artificial Intelligence and Cognitive Science using prototypes, plans, or schemata; formal semantics in natural language, especially the semantics of the `if-then' conditional construct; and the logic of subjunctive conditionals first developed using a possible worlds semantics by Stalnaker and Lewis. The basic premise of the paper is that both schema-based inference and the semantics of conditionals are based on (...)
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  • Position Systems in Dynamic Domains.Jianmin Ji & Fangzhen Lin - 2015 - Journal of Philosophical Logic 44 (2):147-161.
    A dynamic domain consists of a set of legal states and a transition function that maps states to states. AI formalisms for specifying dynamic domains have so far focused on describing the effects of actions, that is, the transition functions. In this paper we propose a notion of characteristic set of position systems for the purpose of describing legal states. A position system for a type of objects is a set of properties that are mutually exclusive, and that in each (...)
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  • ALX, an action logic for agents with bounded rationality.Zhisheng Huang, Michael Masuch & László Pólos - 1996 - Artificial Intelligence 82 (1-2):75-127.
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  • Reasoning about action II.Matthew L. Ginsberg & David E. Smith - 1988 - Artificial Intelligence 35 (3):311-342.
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  • Approximate planning.Matthew L. Ginsberg - 1995 - Artificial Intelligence 76 (1-2):89-123.
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  • Sound and efficient closed-world reasoning for planning.Oren Etzioni, Keith Golden & Daniel S. Weld - 1997 - Artificial Intelligence 89 (1-2):113-148.
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  • Let's plan it deductively!W. Bibel - 1998 - Artificial Intelligence 103 (1-2):183-208.
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