Optimality Principles in Computational Approaches to Conceptual Blending: Do We Need Them (at) All?

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Pedro Martins, Senja Pollak, Tanja Urbancic and Amilcar Cardoso: Optimality Principles in Computational Approaches to Conceptual Blending: Do We Need Them (at) All? In: Computational Creativity 2016 ICCC 2016, 346-353

DOI

Abstract

Optimality principles are a key element in the Conceptual Blending (CB) framework, as they are responsible for guiding the integration process towards ‘good blends’. Despite their relevance, these principles are often overlooked in the design of computational models of CB. In this paper, we analyse the explicit or implicit presence and relevance of the optimality principles in three different computational approaches to the CB, known from the literature. The approaches chosen for the analysis are Divago, Blending from a generalisationbased analogy model, and blending as a convolution of neural patterns. The analysis contains a discussion on the relevance of the principles and how some of absent principles can be introduced in the different models.

Extended Abstract

Bibtex

@inproceedings{
 author = {Pedro Martins, Senja Pollak, Tanja Urbancic and Amilcar Cardoso},
 title = {Optimality Principles in Computational Approaches to Conceptual Blending: Do We Need Them (at) All?},
 booktitle = {Proceedings of the Seventh International Conference on Computational Creativity},
 series = {ICCC2016},
 year = {2016},
 month = {Jun-July},
 location = {Paris, France},
 pages = {346-353},
 url = {http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Optimality-Principles-in-Computational-Approaches-to-Conceptual-Blending.pdf http://de.evo-art.org/index.php?title=Optimality_Principles_in_Computational_Approaches_to_Conceptual_Blending:_Do_We_Need_Them_(at)_All%3F },
 publisher = {Sony CSL Paris},
}


Used References

Bou, F.; M., E.; Plaza, E.; and Schorlemmer, M. 2014. D2.1 - reasoning with amalgams. Public deliverable, Concept Invention Theory (FP7 - 611553).

Fauconnier, G., and Turner, M. 1998. Conceptual integration networks. Cognitive Science 22(2):133–187.

Fauconnier, G., and Turner, M. 2002. The Way We Think. New York: Basic Books.

Fauconnier, G. 1994. Mental Spaces: Aspects of Meaning Construction in Natural Language. New York: Cambridge University Press.

Fauconnier, G. 2005. Compression and emergent structure. Language and Linguistics 6(4):523–538.

Goguen, J. 1999. An introduction to algebraic semiotics, with applications to user interface design. In Lecture Notes in Artificial Intelligence, volume Computation for Metaphor, Analogy and Agents, 242–291. Springer.

Grady, J. E.; Oakley, T.; and Coulson, S. 1999. Blending and metaphor. In Steen, G., and Gibbs, R., eds., Metaphor in Cognitive Linguistics.

Guhe, M.; Pease, A.; Smaill, A.; Martinez, M.; Schmidt, M.; Gust, M.; K¨uhnberger, K.-U.; and Krumnack, U. 2011. A computational account of conceptual blending in basic mathematics. Cognitive Systems Research 12(3–4):249– 265. Special Issue on Complex Cognition.

Gust, H.; K¨uhnberger, K.-U.; and Schmid, U. 2006. Metaphors and heuristic-driven theory projection (hdtp). Theoretical Computer Science 354(1):98 – 117. Algebraic Methods in Language Processing Third International {AMAST} Workshop on Algebraic Methods in Language Processing 2003.

Kowalewski, H. 2008. Conceptual blending and sign formation. The Public Journal of Semiotics 2(2):30–51.

Li, B.; Zook, A.; Davis, N.; and Riedl, M. 2012. Goaldriven conceptual blending: A computational approach for creativity. In Maher, M. L.; Hammond, K.; Pease, A.; P´erez, R.; Ventura, D.; and Wiggins, G., eds., Proceedings of the Third International Conference on Computational Creativity, 9–16.

Martins, P.; Cardoso, A.; Urbanˇciˇc, T.; Pollak, S.; Perovˇsek, M.; and Lavraˇc, N. 2014. Study and design of methods for concept blending. Public deliverable, Concept Creation Technology (FP7 - 611733 ).

Martins, P.; Urbanˇciˇc, T.; Pollak, S.; Lavraˇc, N.; and Cardoso, A. 2015. The good, the bad, and the aha! blends. In Proceedings of the 6th Int. Conference on Computational Creativity, ICCC-15.

Pereira, F. C. 2005. Creativity and AI: A Conceptual Blending approach. Ph.D. Dissertation, University of Coimbra.

Schwering, A.; Krumnack, U.; K¨uhnberger, K.-U.; and Gust, H. 2009. Syntactic principles of heuristic-driven theory projection. Cognitive Systems Research 10(3):251–269.

Special Issue on Analogies - Integrating Cognitive Abilities. Thagard, P., and Stewart, T.


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