Theory blending: extended algorithmic aspects and examples

Martinez, M., Abdel-Fattah, A. M. H., Krumnack, U., Gomez-Ramirez, D., Smaill, A., Besold, T. R., Pease, A., Schmidt, M., Guhe, M. & Kuehnberger, K-U. (2017). Theory blending: extended algorithmic aspects and examples. Annals of Mathematics and Artificial Intelligence, 80(1), pp. 65-89. doi: 10.1007/s10472-016-9505-y

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In Cognitive Science, conceptual blending has been proposed as an important cognitive mechanism that facilitates the creation of new concepts and ideas by constrained combination of available knowledge. It thereby provides a possible theoretical foundation for modeling high-level cognitive faculties such as the ability to understand, learn, and create new concepts and theories. Quite often the development of new mathematical theories and results is based on the combination of previously independent concepts, potentially even originating from distinct subareas of mathematics. Conceptual blending promises to offer a framework for modeling and re-creating this form of mathematical concept invention with computational means. This paper describes a logic-based framework which allows a formal treatment of theory blending (a subform of the general notion of conceptual blending with high relevance for applications in mathematics), discusses an interactive algorithm for blending within the framework, and provides several illustrating worked examples from mathematics.

Item Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in Annals of Mathematics and Artificial Intelligence. The final authenticated version is available online at:
Uncontrolled Keywords: Concept Blending, Heuristic-Driven Theory Projection
Divisions: School of Informatics > Department of Computing

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