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  1. Realism and antirealism in artificial intelligence.David H. Helman - 1987 - British Journal for the Philosophy of Science 38 (1):19-26.
    In the philosophy of mind, the controversy between realists and antirealists often concerns the logical form of sentences embedded in attitude reports. Antirealists believe that such sentences refer to psychological states; realists believe that they refer to situations or states of the world. In this essay, it is shown how these two modes of semantic representation are associated with different approaches to the computational modeling of cognitive processes. I put forward a normative account of methodology in artificial intelligence that reconciles (...)
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  • Commonsense reasoning about containers using radically incomplete information.Ernest Davis, Gary Marcus & Noah Frazier-Logue - 2017 - Artificial Intelligence 248 (C):46-84.
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  • Extracting qualitative relations from categorical data.Jure Žabkar, Ivan Bratko & Janez Demšar - 2016 - Artificial Intelligence 239 (C):54-69.
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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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  • Qualitatively faithful quantitative prediction.Dorian Šuc, Daniel Vladušič & Ivan Bratko - 2004 - Artificial Intelligence 158 (2):189-214.
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  • Chronological ignorance: Experiments in nonmonotonic temporal reasoning.Yoav Shoham - 1988 - Artificial Intelligence 36 (3):279-331.
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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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  • Exaggeration.Daniel S. Weld - 1990 - Artificial Intelligence 43 (3):311-368.
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  • Graphs of models.Sanjaya Addanki, Roberto Cremonini & J. Scott Penberthy - 1991 - Artificial Intelligence 51 (1-3):145-177.
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  • Qualitative superposition.Enrico W. Coiera - 1992 - Artificial Intelligence 56 (2-3):171-196.
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  • 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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  • A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture.David Pierre Leibovitz - 2013 - Dissertation, Carleton University
    The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to raise (...)
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  • Remarks on Simon's Comments.Yoav Shoham - 1991 - Cognitive Science 15 (2):301-303.
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  • 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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  • 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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  • Qualitative analysis of MOS circuits.Brian C. Williams - 1984 - Artificial Intelligence 24 (1-3):281-346.
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  • An assumption-based TMS.Johan de Kleer - 1986 - Artificial Intelligence 28 (2):127-162.
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  • Theories of causal ordering.Johan de Kleer & John Seely Brown - 1986 - Artificial Intelligence 29 (1):33-61.
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  • Comparative analysis.Daniel S. Weld - 1988 - Artificial Intelligence 36 (3):333-373.
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  • Analogical representations of naive physics.Francesco Gardin & Bernard Meltzer - 1989 - Artificial Intelligence 38 (2):139-159.
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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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  • 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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  • Order of magnitude reasoning.Olivier Raiman - 1991 - Artificial Intelligence 51 (1-3):11-38.
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  • 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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  • Images and inference.Robert K. Lindsay - 1988 - Cognition 29 (3):229-250.
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  • Pouring liquids: A study in commonsense physical reasoning.Ernest Davis - 2008 - Artificial Intelligence 172 (12-13):1540-1578.
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  • Constraint propagation with interval labels.Ernest Davis - 1987 - Artificial Intelligence 32 (3):281-331.
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  • Qualitative physics using dimensional analysis.R. Bhaskar & Anil Nigam - 1990 - Artificial Intelligence 45 (1-2):73-111.
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  • Qualitative spatial reasoning: The CLOCK project.Kenneth D. Forbus, Paul Nielsen & Boi Faltings - 1991 - Artificial Intelligence 51 (1-3):417-471.
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  • 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.
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  • Reasoning about model accuracy.Daniel S. Weld - 1992 - Artificial Intelligence 56 (2-3):255-300.
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  • An extension of QSIM with qualitative curvature.Abul Hossain & Kumar S. Ray - 1997 - Artificial Intelligence 96 (2):303-350.
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  • Efficient compositional modeling for generating causal explanations.P. Pandurang Nayak & Leo Joskowicz - 1996 - Artificial Intelligence 83 (2):193-227.
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  • Qualitative rigid-body mechanics.Thomas F. Stahovich, Randall Davis & Howard Shrobe - 2000 - Artificial Intelligence 119 (1-2):19-60.
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  • Generating multiple new designs from a sketch.Thomas F. Stahovich, Randall Davis & Howard Shrobe - 1998 - Artificial Intelligence 104 (1-2):211-264.
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  • An ontological model of device function: industrial deployment and lessons learned.Yoshinobu Kitamura, Yusuke Koji & Riichiro Mizoguchi - 2006 - Applied ontology 1 (3):237-262.
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  • 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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  • 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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  • The scope and limits of simulation in automated reasoning.Ernest Davis & Gary Marcus - 2016 - Artificial Intelligence 233 (C):60-72.
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  • Problem solving with the ATMS.Johan de Kleer - 1986 - Artificial Intelligence 28 (2):197-224.
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  • Causality in device behavior.Yumi Iwasaki & Herbert A. Simon - 1986 - Artificial Intelligence 29 (1):3-32.
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  • Qualitative simulation.Benjamin Kuipers - 1986 - Artificial Intelligence 29 (3):289-338.
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  • Qualitative kinematics in mechanisms.Boi Faltings - 1990 - Artificial Intelligence 44 (1-2):89-119.
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  • Integrating actions and state constraints: A closed-form solution to the ramification problem (sometimes).Sheila A. McIlraith - 2000 - Artificial Intelligence 116 (1-2):87-121.
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  • 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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  • Reasoning with qualitative models.Benjamin J. Kuipers - 1993 - Artificial Intelligence 59 (1-2):125-132.
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  • How circuits work.Johan De Kleer - 1984 - Artificial Intelligence 24 (1-3):205-280.
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  • Modeling digital circuits for troubleshooting.Walter C. Hamscher - 1991 - Artificial Intelligence 51 (1-3):223-271.
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  • 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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  • Mundane reasoning by settling on a plausible model.Mark Derthick - 1990 - Artificial Intelligence 46 (1-2):107-157.
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