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  1. Mundane reasoning by settling on a plausible model.Mark Derthick - 1990 - Artificial Intelligence 46 (1-2):107-157.
  • Theories of causal ordering.Johan de Kleer & John Seely Brown - 1986 - Artificial Intelligence 29 (1):33-61.
  • Problem solving with the ATMS.Johan de Kleer - 1986 - Artificial Intelligence 28 (2):197-224.
  • Model-based reasoning about learner behaviour.Kees de Koning, Bert Bredeweg, Joost Breuker & Bob Wielinga - 2000 - Artificial Intelligence 117 (2):173-229.
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  • How circuits work.Johan De Kleer - 1984 - Artificial Intelligence 24 (1-3):205-280.
  • A perspective on assumption-based truth maintenance.Johan de Kleer - 1993 - Artificial Intelligence 59 (1-2):63-67.
  • An assumption-based TMS.Johan de Kleer - 1986 - Artificial Intelligence 28 (2):127-162.
  • The scope and limits of simulation in automated reasoning.Ernest Davis & Gary Marcus - 2016 - Artificial Intelligence 233 (C):60-72.
  • Pouring liquids: A study in commonsense physical reasoning.Ernest Davis - 2008 - Artificial Intelligence 172 (12-13):1540-1578.
  • Commonsense reasoning about containers using radically incomplete information.Ernest Davis, Gary Marcus & Noah Frazier-Logue - 2017 - Artificial Intelligence 248 (C):46-84.
  • Constraint propagation with interval labels.Ernest Davis - 1987 - Artificial Intelligence 32 (3):281-331.
  • A note on the correctness of the causal ordering algorithm.Denver Dash & Marek J. Druzdzel - 2008 - Artificial Intelligence 172 (15):1800-1808.
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  • Qualitative superposition.Enrico W. Coiera - 1992 - Artificial Intelligence 56 (2-3):171-196.
  • Qualitative physics using dimensional analysis.R. Bhaskar & Anil Nigam - 1990 - Artificial Intelligence 45 (1-2):73-111.
  • Influence-based model decomposition for reasoning about spatially distributed physical systems.Chris Bailey-Kellogg & Feng Zhao - 2001 - Artificial Intelligence 130 (2):125-166.
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  • Graphs of models.Sanjaya Addanki, Roberto Cremonini & J. Scott Penberthy - 1991 - Artificial Intelligence 51 (1-3):145-177.
  • Extracting qualitative relations from categorical data.Jure Žabkar, Ivan Bratko & Janez Demšar - 2016 - Artificial Intelligence 239 (C):54-69.
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  • Learning qualitative models from numerical data.Jure Žabkar, Martin Možina, Ivan Bratko & Janez Demšar - 2011 - Artificial Intelligence 175 (9-10):1604-1619.
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  • CLIP: concept learning from inference patterns.Ken'ichi Yoshida & Hiroshi Motoda - 1995 - Artificial Intelligence 75 (1):63-92.
  • Qualitative reasoning about physical systems: A return to roots.Brian C. Williams & Johan de Kleer - 1991 - Artificial Intelligence 51 (1-3):1-9.
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  • Qualitative analysis of MOS circuits.Brian C. Williams - 1984 - Artificial Intelligence 24 (1-3):281-346.
  • Causal model progressions as a foundation for intelligent learning environments.Barbara Y. White & John R. Frederiksen - 1990 - Artificial Intelligence 42 (1):99-157.
  • Approximate truth.Thomas Weston - 1987 - Journal of Philosophical Logic 16 (2):203 - 227.
    The technical results presented here on continuity and approximate implication are obviously incomplete. In particular, a syntactic characterization of approximate implication is highly desirable. Nevertheless, I believe the results above do show that the theory has considerable promise for application to the areas mentioned at the top of the paper.Formulation and defense of realist interpretations of science, for example, require approximate truth because we hardly ever have evidence that a particular scientific theory corresponds perfectly with a portion of the real (...)
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  • The use of aggregation in causal simulation.Daniel S. Weld - 1986 - Artificial Intelligence 30 (1):1-34.
  • Reasoning about model accuracy.Daniel S. Weld - 1992 - Artificial Intelligence 56 (2-3):255-300.
  • Exaggeration.Daniel S. Weld - 1990 - Artificial Intelligence 43 (3):311-368.
  • Comparative analysis.Daniel S. Weld - 1988 - Artificial Intelligence 36 (3):333-373.
  • Qualitatively faithful quantitative prediction.Dorian Šuc, Daniel Vladušič & Ivan Bratko - 2004 - Artificial Intelligence 158 (2):189-214.
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  • Qualitative rigid-body mechanics.Thomas F. Stahovich, Randall Davis & Howard Shrobe - 2000 - Artificial Intelligence 119 (1-2):19-60.
  • Generating multiple new designs from a sketch.Thomas F. Stahovich, Randall Davis & Howard Shrobe - 1998 - Artificial Intelligence 104 (1-2):211-264.
  • Remarks on Simon's Comments.Yoav Shoham - 1991 - Cognitive Science 15 (2):301-303.
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  • Chronological ignorance: Experiments in nonmonotonic temporal reasoning.Yoav Shoham - 1988 - Artificial Intelligence 36 (3):279-331.
  • Qualitative system identification: deriving structure from behavior.A. C. Cem Say & Selahattin Kuru - 1996 - Artificial Intelligence 83 (1):75-141.
  • A dynamic systems perspective on qualitative simulation.Elisha Sacks - 1990 - Artificial Intelligence 42 (2-3):349-362.
  • Rough intervals—enhancing intervals for qualitative modeling of technical systems.M. Rebolledo - 2006 - Artificial Intelligence 170 (8-9):667-685.
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  • Order of magnitude reasoning.Olivier Raiman - 1991 - Artificial Intelligence 51 (1-3):11-38.
  • Efficient compositional modeling for generating causal explanations.P. Pandurang Nayak & Leo Joskowicz - 1996 - Artificial Intelligence 83 (2):193-227.
  • Causal approximations.P. Pandurang Nayak - 1994 - Artificial Intelligence 70 (1-2):277-334.
  • Causality as a key to the frame problem.Hideyuki Nakashima, Hitoshi Matsubara & Ichiro Osawa - 1997 - Artificial Intelligence 91 (1):33-50.
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  • A comprehensive methodology for building hybrid models of physical systems.Pieter J. Mosterman & Gautam Biswas - 2000 - Artificial Intelligence 121 (1-2):171-209.
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  • Mid-sized axiomatizations of commonsense problems: A case study in egg cracking.Leora Morgenstern - 2001 - Studia Logica 67 (3):333-384.
    We present an axiomatization of a problem in commonsense reasoning, characterizing the proper procedure for cracking an egg and transferring its contents to a bowl. The axiomatization is mid-sized, larger than toy problems such as the Yale Shooting Problem or the Suitcase Problem, but much smaller than the comprehensive axiomatizations associated with CYC and HPKB. This size of axiomatization permits the development of non-trivial, reusable core theories of commonsense reasoning, acts as a testbed for existing theories of commonsense reasoning, and (...)
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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.
  • Images and inference.Robert K. Lindsay - 1988 - Cognition 29 (3):229-250.
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  • Diagnosis based on explicit means-end models.Jan Eric Larsson - 1996 - Artificial Intelligence 80 (1):29-93.
  • Qualitative simulation.Benjamin Kuipers - 1986 - Artificial Intelligence 29 (3):289-338.
  • Reasoning with qualitative models.Benjamin J. Kuipers - 1993 - Artificial Intelligence 59 (1-2):125-132.
  • Higher-order derivative constraints in qualitative simulation.Benjamin J. Kuipers, Charles Chiu, David T. Dalle Molle & D. R. Throop - 1991 - Artificial Intelligence 51 (1-3):343-379.
  • A framework for explaining decision-theoretic advice.David A. Klein & Edward H. Shortliffe - 1994 - Artificial Intelligence 67 (2):201-243.
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  • From the textual description of an accident to its causes.Daniel Kayser & Farid Nouioua - 2009 - Artificial Intelligence 173 (12-13):1154-1193.
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  • Causality in device behavior.Yumi Iwasaki & Herbert A. Simon - 1986 - Artificial Intelligence 29 (1):3-32.