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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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  • Qualitative process theory.Kenneth D. Forbus - 1984 - Artificial Intelligence 24 (1-3):85-168.
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  • Qualitative simulation.Benjamin Kuipers - 1986 - Artificial Intelligence 29 (3):289-338.
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  • Applications of Circumscription to Formalizing Common Sense Knowledge.John McCarthy - 1986 - Artificial Intelligence 28 (1):89–116.
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  • A dynamic systems perspective on qualitative simulation.Elisha Sacks - 1990 - Artificial Intelligence 42 (2-3):349-362.
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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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  • Creativity and learning in a case-based explainer.Roger C. Schank & David B. Leake - 1989 - Artificial Intelligence 40 (1-3):353-385.
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  • Formalizing nonmonotonic reasoning systems.David W. Etherington - 1987 - Artificial Intelligence 31 (1):41-85.
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  • Transcending inductive category formation in learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):639-651.
    The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial (...)
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