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  1. A Temporal Logic for Reasoning about Processes and Plans.Drew McDermott - 1982 - Cognitive Science 6 (2):101-155.
    Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first‐order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It (...)
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  • A logic for default reasoning.Ray Reiter - 1980 - Artificial Intelligence 13 (1-2):81-137.
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  • (1 other version)On the logic of theory change: Partial meet contraction and revision functions.Carlos E. Alchourrón, Peter Gärdenfors & David Makinson - 1985 - Journal of Symbolic Logic 50 (2):510-530.
    This paper extends earlier work by its authors on formal aspects of the processes of contracting a theory to eliminate a proposition and revising a theory to introduce a proposition. In the course of the earlier work, Gardenfors developed general postulates of a more or less equational nature for such processes, whilst Alchourron and Makinson studied the particular case of contraction functions that are maximal, in the sense of yielding a maximal subset of the theory (or alternatively, of one of (...)
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  • Intelligence without representation.Rodney A. Brooks - 1991 - Artificial Intelligence 47 (1--3):139-159.
    Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...)
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  • Non-monotonic logic I.Drew McDermott & Jon Doyle - 1980 - Artificial Intelligence 13 (1-2):41-72.
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  • Nonmonotonic reasoning, preferential models and cumulative logics.Sarit Kraus, Daniel Lehmann & Menachem Magidor - 1990 - Artificial Intelligence 44 (1-2):167-207.
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  • Circumscription — A Form of Non-Monotonic Reasoning.John McCarthy - 1980 - Artificial Intelligence 13 (1-2):27–39.
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  • Two approaches to the formalisation of defeasible deontic reasoning.Henry Prakken - 1996 - Studia Logica 57 (1):73 - 90.
    This paper compares two ways of formalising defeasible deontic reasoning, both based on the view that the issues of conflicting obligations and moral dilemmas should be dealt with from the perspective of nonmonotonic reasoning. The first way is developing a special nonmonotonic logic for deontic statements. This method turns out to have some limitations, for which reason another approach is recommended, viz. combining an already existing nonmonotonic logic with a deontic logic. As an example of this method the language of (...)
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  • Uniform semantic treatment of default and autoepistemic logics.Marc Denecker, Victor W. Marek & Mirosław Truszczyński - 2003 - Artificial Intelligence 143 (1):79-122.
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  • (1 other version)Some Philosophical Problems from the Standpoint of Artificial Intelligence.J. McCarthy & P. J. Hayes - 1969 - Machine Intelligence 4:463-502.
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  • Ramification and causality.Michael Thielscher - 1997 - Artificial Intelligence 89 (1-2):317-364.
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  • A Spatial Logic Based on Regions and Connection.David Randell, Cui A., Cohn Zhan & G. Anthony - 1992 - KR 92:165--176.
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  • Semantic Considerations on nonmonotonic Logic.Robert C. Moore - 1985 - Artificial Intelligence 25 (1):75-94.
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  • Nonmonotonic logic and temporal projection.Steve Hanks & Drew McDermott - 1987 - Artificial Intelligence 33 (3):379-412.
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  • On the relation between default and autoepistemic logic.Kurt Konolige - 1988 - Artificial Intelligence 35 (3):343-382.
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  • Intention is choice with commitment.Philip R. Cohen & Hector J. Levesque - 1990 - Artificial Intelligence 42 (2-3):213-261.
    This paper explores principles governing the rational balance among an agent's beliefs, goals, actions, and intentions. Such principles provide specifications for artificial agents, and approximate a theory of human action (as philosophers use the term). By making explicit the conditions under which an agent can drop his goals, i.e., by specifying how the agent is committed to his goals, the formalism captures a number of important properties of intention. Specifically, the formalism provides analyses for Bratman's three characteristic functional roles played (...)
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  • Answer set programming and plan generation.Vladimir Lifschitz - 2002 - Artificial Intelligence 138 (1-2):39-54.
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