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Towards a Computational Framework for Function-Driven Concept Invention

  • Nico PotykaEmail author
  • Danny Gómez-Ramírez
  • Kai-Uwe Kühnberger
Conference paper
  • 1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9782)

Abstract

We propose a novel framework for computational concept invention. As opposed to recent implementations of Fauconnier’s and Turner’s Conceptual Blending Theory, our framework simplifies computational concept invention by focusing on concepts’ functions rather than on structural similarity of concept descriptions. Even though creating an optimal combination of concepts that achieves the desired functions is NP-complete in general, some interesting special cases are tractable.

Keywords

Utility Function Functional Unit Input Space Local Algorithm Concept Invention 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Some of the authors acknowledge the financial support of the Future and Emerging Technologies Programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open grant number: 611553 (COINVENT).

References

  1. 1.
    Aerts, D., Gabora, L.: A theory of concepts and their combinations ii: a hilbert space representation. Kybernetes 34(1/2), 192–221 (2005)CrossRefzbMATHGoogle Scholar
  2. 2.
    Besold, T., Kühnberger, K.-U., Plaza, E.: Analogy, amalgams, and concept blending. In: Proceedings of ACS 2015, p. 23 (2015)Google Scholar
  3. 3.
    Downey, R.G., Fellows, M.R.: Parameterized complexity. Springer, Heidelberg (2012)zbMATHGoogle Scholar
  4. 4.
    Eppe, M., Confalonieri, R., Maclean, E., Kaliakatsos-Papakostas, M.A., Cambouropoulos, E., Schorlemmer, W.M., Codescu, M., Kühnberger, K.: Computational invention of cadences and chord progressions by conceptual chord-blending. In: Proceedings of IJCAI 2015, pp. 2445–2451. AAAI Press (2015)Google Scholar
  5. 5.
    Fauconnier, G., Turner, M.: Conceptual integration networks. Cogn. Sci. 22(2), 133–187 (1998)CrossRefGoogle Scholar
  6. 6.
    Fauconnier, G., Turner, M.: The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. Basic Books, New York (2008)Google Scholar
  7. 7.
    Goguen, J.A., Harrell, D.F.: Style: a computational and conceptual blending-based approach. In: Argamon, S., Burns, K., Dubnov, S. (eds.) The Structure of Style, pp. 291–316. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Guhe, M., Pease, A., Smaill, A., Martinez, M., Schmidt, M., Gust, H., Kühnberger, K.-U., Krumnack, U.: A computational account of conceptual blending in basic mathematics. Cogn. Syst. Res. 12(3–4), 249–265 (2011)CrossRefGoogle Scholar
  9. 9.
    Li, B., Zook, A., Davis, N., Riedl, M.O.: Goal-driven conceptual blending: a computational approach for creativity. In: International Conference on Computational Creativity, vol.10 (2012)Google Scholar
  10. 10.
    Martinez, M., Krumnack, U., Smaill, A., Besold, T.R., Abdel-Fattah, A.M.H., Schmidt, M., Gust, H., Kühnberger, K.-U., Guhe, M., Pease, A.: Algorithmic aspects of theory blending. In: Aranda-Corral, G.A., Calmet, J., Martín-Mateos, F.J. (eds.) AISC 2014. LNCS, vol. 8884, pp. 180–192. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. logic Program. 19, 629–679 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Neuhaus, F., Kutz, O., Codescu, M., Mossakowski, T.: Fabricating monsters is hard: towards the automation of conceptual blending. In: Proceedings of the C3GI@ ECAI-14, vol. 1 (2014)Google Scholar
  13. 13.
    Pereira, F.C.: Creativity and artificial intelligence: a conceptual blending approach, vol. 1. Walter de Gruyter, Hawthorne (2007)Google Scholar
  14. 14.
    Popescul, A., Ungar, L.H.: Cluster-based concept invention for statistical relational learning. In: Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 665–670. ACM (2004)Google Scholar
  15. 15.
    Schorlemmer, M., Smaill, A., Kühnberger, K.-U., Kutz, O., Colton, S., Cambouropoulos, E., Pease, A.: Coinvent: towards a computational concept invention theory. In: Proceedings of the ICCC 2014 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nico Potyka
    • 1
    Email author
  • Danny Gómez-Ramírez
    • 1
  • Kai-Uwe Kühnberger
    • 1
  1. 1.Institute of Cognitive ScienceUniversity of OsnabrückOsnabrückGermany

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