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  1. 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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  • Reasoning about model accuracy.Daniel S. Weld - 1992 - Artificial Intelligence 56 (2-3):255-300.
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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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  • The roles of associational and causal reasoning in problem solving.Reid G. Simmons - 1992 - Artificial Intelligence 53 (2-3):159-207.
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  • Reading agendas between the lines, an exercise.Giovanni Sileno, Alexander Boer & Tom van Engers - 2017 - Artificial Intelligence and Law 25 (1):89-106.
    This work presents elements for an alternative operationalization of monitoring and diagnosis of multi-agent systems, developed in the context of compliance checking. In contrast to traditional accounts of model-based diagnosis, and most proposals concerning non-compliance, our method does not consider any commitment towards the individual unit of agency. Identity is considered to be mostly an attribute to assign responsibility, and not as the only referent to a source of intentionality. The proposed method requires as input a set of prototypical agent-roles (...)
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  • Goal-directed diagnosis—a diagnostic reasoning framework for exploratory-corrective domains.Ron Rymon - 1996 - Artificial Intelligence 84 (1-2):257-297.
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  • Automated modeling of complex systems to answer prediction questions.Jeff Rickel & Brace Porter - 1997 - Artificial Intelligence 93 (1-2):201-260.
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  • A theory of diagnosis from first principles.Raymond Reiter - 1987 - Artificial Intelligence 32 (1):57-95.
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  • The Case for Psychologism in Default and Inheritance Reasoning.Francis Jeffry Pelletier & Renée Elio - 2005 - Synthese 146 (1-2):7-35.
    Default reasoning occurs whenever the truth of the evidence available to the reasoner does not guarantee the truth of the conclusion being drawn. Despite this, one is entitled to draw the conclusion “by default” on the grounds that we have no information which would make us doubt that the inference should be drawn. It is the type of conclusion we draw in the ordinary world and ordinary situations in which we find ourselves. Formally speaking, ‘nonmonotonic reasoning’ refers to argumentation in (...)
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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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  • Causal approximations.P. Pandurang Nayak - 1994 - Artificial Intelligence 70 (1-2):277-334.
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  • Remote Agent: to boldly go where no AI system has gone before.Nicola Muscettola, P. Pandurang Nayak, Barney Pell & Brian C. Williams - 1998 - Artificial Intelligence 103 (1-2):5-47.
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  • Speeding up inferences using relevance reasoning: a formalism and algorithms.Alon Y. Levy, Richard E. Fikes & Yehoshua Sagiv - 1997 - Artificial Intelligence 97 (1-2):83-136.
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  • Diagnosis based on explicit means-end models.Jan Eric Larsson - 1996 - Artificial Intelligence 80 (1):29-93.
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  • Model-based diagnostics and probabilistic assumption-based reasoning.J. Kohlas, B. Anrig, R. Haenni & P. A. Monney - 1998 - Artificial Intelligence 104 (1-2):71-106.
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  • A theory of measurement in diagnosis from first principles.Aimin Hou - 1994 - Artificial Intelligence 65 (2):281-328.
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  • Modeling digital circuits for troubleshooting.Walter C. Hamscher - 1991 - Artificial Intelligence 51 (1-3):223-271.
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  • Some fundamental properties of local constraint propagation.Hans-Werner Güsgen & Joachim Hertzberg - 1988 - Artificial Intelligence 36 (2):237-247.
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  • Model-based computing: Developing flexible machine control software.Markus P. J. Fromherz, Vijay A. Saraswat & Daniel G. Bobrow - 1999 - Artificial Intelligence 114 (1-2):157-202.
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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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  • EXCALIBUR: a program for planning and reasoning with processes.Brian Drabble - 1993 - Artificial Intelligence 62 (1):1-40.
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  • Mathematical problems arising in qualitative simulation of a differential equation.Olivier Dordan - 1992 - Artificial Intelligence 55 (1):61-86.
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  • Using crude probability estimates to guide diagnosis.Johan de Kleer - 1990 - Artificial Intelligence 45 (3):381-391.
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  • Diagnosing multiple faults.Johan de Kleer & Brian C. Williams - 1987 - Artificial Intelligence 32 (1):97-130.
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  • Characterizing diagnoses and systems.Johan de Kleer, Alan K. Mackworth & Raymond Reiter - 1992 - Artificial Intelligence 56 (2-3):197-222.
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  • Structure-driven algorithms for truth maintenance.Rina Dechter & Avi Dechter - 1996 - Artificial Intelligence 82 (1-2):1-20.
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  • Dynamic across-time measurement interpretation.Dennis DeCoste - 1991 - Artificial Intelligence 51 (1-3):273-341.
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  • Retrospective on “Diagnostic reasoning based on structure and behavior”.Randall Davis - 1993 - Artificial Intelligence 59 (1-2):149-157.
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  • Process algebras for systems diagnosis.Luca Console, Claudia Picardi & Marina Ribaudo - 2002 - Artificial Intelligence 142 (1):19-51.
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  • Generating and generalizing models of visual objects.Jonathan H. Connell & Michael Brady - 1987 - Artificial Intelligence 31 (2):159-183.
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  • Qualitative superposition.Enrico W. Coiera - 1992 - Artificial Intelligence 56 (2-3):171-196.
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  • Hierarchical model-based diagnosis based on structural abstraction.Luca Chittaro & Roberto Ranon - 2004 - Artificial Intelligence 155 (1-2):147-182.
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  • A spectrum of definitions for temporal model-based diagnosis.Vittorio Brusoni, Luca Console, Paolo Terenziani & Daniele Theseider Dupré - 1998 - Artificial Intelligence 102 (1):39-79.
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  • Abduction as belief revision.Craig Boutilier & Veronica Beche - 1995 - Artificial Intelligence 77 (1):43-94.
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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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