Generalize and Blend: Concept Blending Based on Generalization, Analogy, and Amalgams

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Tarek R. Besold and Enric Plaza: Generalize and Blend: Concept Blending Based on Generalization, Analogy, and Amalgams. In: Computational Creativity 2015 ICCC 2015, 150-157.

DOI

Abstract

Concept blending, a cognitive process which allows for the combination of certain elements (and their relations) from originally distinct conceptual spaces into a new unified space combining these previously separate elements and allowing the performance of reasoning and inference over the combination, is taken as a key element of creative thought and combinatorial creativity. In this paper, we provide an intermediate report on work towards the development of a computational-level and algorithmic-level account of concept blending, presenting the theoretical background together with the main model characteristics, as well as two case studies.

Extended Abstract

Bibtex

@inproceedings{
 author = {Besold, Tarek R. and Plaza, Enric},
 title = {Generalize and Blend: Concept Blending Based on Generalization, Analogy, and Amalgams},
 booktitle = {Proceedings of the Sixth International Conference on Computational Creativity},
 series = {ICCC2015},
 year = {2015},
 month = {Jun},
 location = {Park City, Utah, USA},
 pages = {150-157},
 url = {http://computationalcreativity.net/iccc2015/proceedings/7_1Besold.pdf http://de.evo-art.org/index.php?title=Generalize_and_Blend:_Concept_Blending_Based_on_Generalization,_Analogy,_and_Amalgams },
 publisher = {International Association for Computational Creativity},
 keywords = {computational, creativity},
}

Used References

Aamodt, A., and Plaza, E. 1994. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7(1):39–59.

Besold, T. R. 2014. Sensorimotor Analogies in Learning Abstract Skills and Knowledge: Modeling Analogy- Supported Education in Mathematics and Physics. In Proc. of the AAAI Fall 2014 Symposium on Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, volume FS-14-05 of AAAI Press Technical Reports.

Boden, M. A. 2003. The Creative Mind: Myths and Mechanisms. Routledge.

Falkenhainer, B.; Forbus, K.; and Gentner, D. 1989. The structure-mapping engine: Algorithm and examples. Artificial Intelligence 41(1):1 – 63.

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

Gentner, D., and Smith, L. A. 2013. Analogical learning and reasoning. In Reisberg, D., ed., The Oxford Handbook of Cognitive Psychology. Oxford University Press. 668–681.

Goguen, J. A., and Harrell, D. F. 2010. Style: A Computational and Conceptual Blending-Based Approach. In Argamon, S.; Burns, K.; and Dubnov, S., eds., The Structure of Style. Springer. 291–316.

Goguen, J. 2006. Mathematical models of cognitive space and time. In Andler, D.; Ogawa, Y.; Okada, M.; and Watanabe, S., eds., Reasoning and Cognition; Proc. of the Interdisciplinary Conference Series on Reasoning Studies, 125–128.

Guhe, M.; Pease, A.; Smaill, A.; Schmidt, M.; Gust, H.; K¨uhnberger, K.-U.; and Krumnack, U. 2010. Mathematical reasoning with higher-order anti-unification. In Proc. of the 32nd Annual Conference of the Cognitive Science Society, 1992–1997. Cognitive Science Society.

Hofstadter, D., and Mitchell, M. 1994. The Copycat project: a model of mental fluidity and analogy-making. In Advances in Connectionist and Neural Computation Theory, volume 2: Analogical Connections, 31–112. Ablex.

Kutz, O.; Mossakowski, T.; Hois, J.; Bhatt, M.; and Bateman, J. 2012. Ontological Blending in DOL. In Proc. of the 1st International Workshop on “Computational Creativity, Concept Invention, and General Intelligence”, Publications of the Institute of Cognitive Science, Univ. of Osnabr¨uck.

Kutz, O.; Bateman, J.; Neuhaus, F.; Mossakowski, T.; and Bhatt, M. 2015. E Pluribus Unum. In Besold, T. R.; Schorlemmer, M.; and Smaill, A., eds., Computational Creativity Research: Towards Creative Machines, volume 7 of Atlantis Thinking Machines. Atlantis Press. 167–196.

Li, B.; Zook, A.; Davis, N.; and Riedl, M. 2012. Goal- Driven Conceptual Blending: A Computational Approach for Creativity. In Proc. of the Third International Conference on Computational Creativity, 9–16.

Martinez, M.; Besold, T. R.; Abdel-Fattah, A.; Gust, H.; Schmidt, M.; Krumnack, U.; and K¨uhnberger, K.-U. 2012. Theory Blending as a Framework for Creativity in Systems for General Intelligence. InWang, P., and Goertzel, B., eds., Theoretical Foundations of Artificial General Intelligence. Atlantis Press. 219–239.

Martinez, M.; Krumnack, U.; Smaill, A.; Besold, T. R.; Abdel-Fattah, A. M.; Schmidt, M.; Gust, H.; K¨uhnberger, K.-U.; Guhe, M.; and Pease, A. 2014. Algorithmic Aspects of Theory Blending. In Aranda-Corral, G.; Calmet, J.; and Mart´ın-Mateos, F., eds., Artificial Intelligence and Symbolic Computation, volume 8884 of LNCS. Springer. 180–192.

Ontan´on, S., and Plaza, E. 2010. Amalgams: A Formal Approach for Combining Multiple Case Solutions. In Bichindaritz, I., and Montani, S., eds., Case-Based Reasoning. Research and Development, volume 6176 of LNCS. Springer. 257–271.

Ontan´on, S., and Plaza, E. 2012. On Knowledge Transfer in Case-Based Inference. In Agudo, B. D., andWatson, I., eds., Case-Based Reasoning Research and Development, volume 7466 of LNCS. Springer. 312–326.

Pereira, F. C. 2007. Creativity and AI: A Conceptual Blending Approach. Mouton de Gruyter.

Schmidt, M.; Krumnack, U.; Gust, H.; and K¨uhnberger, K.-U. 2014. Heuristic-Driven Theory Projection: An Overview. In Prade, H., and Richard, G., eds., Computational Approaches to Analogical Reasoning: Current Trends. Springer. 163–194.

Schorlemmer, M.; Smaill, A.; K¨uhnberger, K.-U.; Kutz, O.; Colton, S.; Cambouropoulos, E.; and Pease, A. 2014. COINVENT: Towards a Computational Concept Invention Theory. In Proc. of the 5th International Conference on Computational Creativity, Ljubljana, Slovenia.

Schwering, A.; Krumnack, U.; K¨uhnberger, K.-U.; and Gust, H. 2009. Syntactic Principles of Heuristic-Driven Theory Projection. Journal of Cognitive Systems Research 10(3):251–269.

Smaling, A. 2003. Inductive, analogical, and communicative generalization. International Journal of Qualitative Methods 2(1).

Thagard, P., and Stewart, T. C. 2011. The AHA! Experience: Creativity Through Emergent Binding in Neural Networks. Cognitive Science 35(1):1–33.

Veale, T., and O’Donoghue, D. 2000. Computation and Blending. Cognitive Linguistics 11(3/4):253–281.

Winston, P. H. 1980. Learning and Reasoning by Analogy. Commun. ACM 23(12):689–703.


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