Abstract
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. This paper describes a logic-based framework which allows a formal treatment of theory blending, discusses algorithmic aspects of blending within the framework, and provides an illustrating worked out example from mathematics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alexander, J.: Blending in Mathematics. Semiotica 2011(187), 1–48 (2011)
Fauconnier, G., Turner, M.: The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. Basic Books, New York (2002)
Goguen, J.: Mathematical models of cognitive space and time. In: Andler, D., Ogawa, Y., Okada, M., Watanabe, S. (eds.) Reasoning and Cognition: Proc. of the Interdisciplinary Conference on Reasoning and Cognition, pp. 125–128. Keio University Press (2006)
Kutz, O., Bateman, J., Neuhaus, F., Mossakowski, T., Bhatt, M.: E pluribus unum: Formalisation, Use-Cases, and Computational Support for Conceptual Blending. In: Computational Creativity Research: Towards Creative Machines, Atlantis Thinking Machines, vol. 7. Springer (forthcoming in, 2015), http://www.springer.com/computer/ai/book/978-94-6239-084-3
Lakoff, G., Núñez, R.: Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being. Basic Books, New York (2000)
Lee, M., Barnden, J.: A Computational Approach to Conceptual Blending within Counterfactuals. Cognitive Science Research Papers CSRP-01-10, School of Computer Science, University of Birmingham (2001)
Manna, Z., Zarba, C.G.: Combining Decision Procedures. In: Aichernig, B.K. (ed.) Formal Methods at the Crossroads. From Panacea to Foundational Support. LNCS, vol. 2757, pp. 381–422. Springer, Heidelberg (2003)
Pereira, F.C.: Creativity and AI: A Conceptual Blending Approach. Applications of Cognitive Linguistics (ACL). Mouton de Gruyter, Berlin (2007)
Plotkin, G.D.: A Note on Inductive Generalization. Machine Intelligence 5, 153–163 (1970)
Schmidt, M., Krumnack, U., Gust, H., Kühnberger, K.-U.: Heuristic-Driven Theory Projection: An Overview. In: Computational Approaches to Analogical Reasoning: Current Trends. SCI, vol. 548, pp. 163–194. Springer, Heidelberg (2014)
Veale, T., O’Donoghue, D.: Computation and blending. Computational Linguistics 11(3-4), 253–282, (2000), Special issue on Conceptual Blending
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Martinez, M. et al. (2014). Algorithmic Aspects of Theory Blending. In: Aranda-Corral, G.A., Calmet, J., Martín-Mateos, F.J. (eds) Artificial Intelligence and Symbolic Computation. AISC 2014. Lecture Notes in Computer Science(), vol 8884. Springer, Cham. https://doi.org/10.1007/978-3-319-13770-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-13770-4_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13769-8
Online ISBN: 978-3-319-13770-4
eBook Packages: Computer ScienceComputer Science (R0)