Elsenbroich, C.; Kutz, O. & Sattler, U. (2006), A case for abductive reasoning over ontologies, in Bernardo Cuenca Grau; Pascal Hitzler; Conor Shankey & Evan Wallace, ed., 'Proceedings of the OWLED*06 Workshop on OWL: Experiences and Directions' .
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We argue for the usefulness of abductive reasoning in the context of ontologies. We discuss several applicaton scenarios in which various forms of abduction would be useful, introduce corresponding abductive reasoning tasks, give examples, and begin to develop the formal apparatus needed to employ abductive inference in expressive description logics.

Holzer, M.; Hörnlein, A.; Atzmueller, M.; Singer, R.; Schlott, S.; Leven, F.-J.; Puppe, F. & Fischer, M. R. (2005), Interoperability of Case-Based Training Systems in Medicine: The CASEPORT Approach, in 'Proc. 9th Workshop of the GMDS AG' , Quintessenz Verlags-GmbH, Berlin, Germany .
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2004

Atzmueller, M.; Shi, W.; Baumeister, J.; Puppe, F. & Barnden, J. A. (2004), Case-Based Approaches for Diagnosing Multiple Disorders, in Valerie Barr & Zdravko Markov, ed., 'Proc. 17th Intl. Florida Artificial Intelligence Research Society Conference 2004 (FLAIRS-2004)' , AAAI Press, , pp. 154--159 .
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Holzer, M.; Singer, R.; Schlott, S.; Hörnlein, A. & Atzmueller, M. (2004), Erfahrungen mit der Entwicklung des CASEPORT-Lernportals, in 'Proc. 8th Workshop of the GMDS AG' , Shaker Verlag, Aachen, Germany .
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2003

Atzmueller, M.; Baumeister, J. & Puppe, F. (2003), Evaluation of two Strategies for Case-Based Diagnosis handling Multiple Faults, in 'Proc. 2nd Conf. Professional Knowledge Management (WM2003)' .
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2000

Leake, D. B. (2000), Case-based reasoning, in Anthony Ralston; Edwin D. Reilly & David Hemmendinger, ed., 'Encyclopedia of Computer Science' , John Wiley and Sons Ltd., Chichester, UK , pp. 196--197 .
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Case-based reasoning(CBR) is an artificial intelligence paradigm for reasoning and learning. Case-based reasoning solves new problems by retrieving stored records of prior problem-solving episodes (cases) and adapting their solutions to fit new circumstances. Each processing episode provides a new case that is stored for future reuse, making learning a natural side-effect of the reasoning process. Case-based reasoning is also studied within cognitive science as a model of human reasoning: studies show that people use recollections of prior problems to guide their reasoning in a wide range of tasks, such as programming, mathematical problem solving, diagnosis, decision making, and design.

Meyer, B. (2000), 'A constraint-based framework for diagrammatic reasoning', Applied Artificial Intelligence 14 , 327--344 .
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