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  1. Shuttling Between Depictive Models and Abstract Rules: Induction and Fallback.Daniel L. Schwartz & John B. Black - 1996 - Cognitive Science 20 (4):457-497.
    A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...)
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  • Causal model progressions as a foundation for intelligent learning environments.Barbara Y. White & John R. Frederiksen - 1990 - Artificial Intelligence 42 (1):99-157.
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  • The topology of boundaries.Margaret M. Fleck - 1996 - Artificial Intelligence 80 (1):1-27.
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  • Comparative analysis.Daniel S. Weld - 1988 - Artificial Intelligence 36 (3):333-373.
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  • Automated reasoning about machines.Andrew Gelsey - 1995 - Artificial Intelligence 74 (1):1-53.
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  • Cmpositional modeling: finding the right model for the job.Brian Falkenhainer & Kenneth D. Forbus - 1991 - Artificial Intelligence 51 (1-3):95-143.
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  • Reasoning about model accuracy.Daniel S. Weld - 1992 - Artificial Intelligence 56 (2-3):255-300.
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  • Knowledge assimilation in domains of actions: a possible causes approach.Renwei Li & Luís Moniz Pereira - 1997 - Journal of Applied Non-Classical Logics 7 (1-2):77-116.
    ABSTRACT One major problem in the process of knowledge assimilation is how to deal with inconsistency of new knowledge and the existing knowledge base. In this paper we present a formal, provably correct and yet computational methodology for assimilation of new knowledge into knowledge bases about actions and changes based on the slogan: what is believed is what is explained. Technically, we employ Gelfond and Lifschitz' action description language A to describe domains of actions. The knowledge bases on domains of (...)
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  • Chronological ignorance: Experiments in nonmonotonic temporal reasoning.Yoav Shoham - 1988 - Artificial Intelligence 36 (3):279-331.
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  • Causal approximations.P. Pandurang Nayak - 1994 - Artificial Intelligence 70 (1-2):277-334.
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