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  1. The power of physical representations.Varol Akman & Paul J. W. ten Hagen - 1989 - AI Magazine 10 (3):49-65.
    Commonsense reasoning about the physical world, as exemplified by "Iron sinks in water" or "If a ball is dropped it gains speed," will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where (...)
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  • A critical examination of Allen's theory of action and time.Antony Galton - 1990 - Artificial Intelligence 42 (2-3):159-188.
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  • Mathematical Model Building in the Solution of Mechanics Problems: Human Protocols and the MECHO Trace.George F. Luger - 1981 - Cognitive Science 5 (1):55-77.
    This paper describes model building and manipulation in the solution of problems in mechanics. An automatic problem solver, MECHO, solving problems in several areas of mechanics, employs (1) a knowledge base representing the semantic content of the particular problem area, (2) a means-ends search strategy similar to GPS to produce sets of simultaneous equations and (3) a “focusing” technique, based on the data within the knowledge base, to guide the GSP-like search through possible equation instantiations. Sets of predicate logic statements (...)
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