Towards a computational- and algorithmic-level account of concept blending using analogies and amalgams

References

• Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.
   Web of Science ®Google Scholar
• Besold, T. R. (In press). The relation of conceptual blending and analogical reasoning. In R. Confalonieri et al. (Eds.), Concept invention: Foundations, implementation, social aspects and applications. Springer.
   Google Scholar
• Besold, T. R., Kühnberger, K.-U., & Plaza, E. (2015). Analogy, amalgams, and concept blending. Proceedings of the third annual conference on advances in cognitive systems (poster collection). Cogsys.org.
   Google Scholar
• Besold, T. R., & Plaza, E. (2015). Generalize and blend: Concept blending based on generalization, analogy, and amalgams. Proceedings of the sixth international conference on computational creativity (ICCC 2015) (pp. 150–157). Provo, UT: Brigham Young University.
   Google Scholar
• Boden, M. (1977). Artificial intelligence and natural man. Hassocks, Sussex: Harvester Press.
   Google Scholar
• Boden, M. A. (2003). The creative mind: Myths and mechanisms. London: Routledge.
   Google Scholar
• Bou, F., Schorlemmer, M., Corneli, J., Gomez Ramirez, D., Maclean, E., Smaill, A., & Pease, A. (2015). The role of blending in mathematical invention. Proceedings of the sixth international conference on computational creativity (ICCC 2015) (pp. 55–62). Park City, UT: Brigham Young University.
   Google Scholar
• Confalonieri, R., Corneli, J., Pease, A., Plaza, E., & Schorlemmer, M. (2015). Using argumentation to evaluate concept blends in combinatorial creativity. Proceedings of the sixth international conference on computational creativity (ICCC 2015) (pp. 174–181). Park City, UT: Brigham Young University.
   Google Scholar
• Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41(1), 1–63. doi: 10.1016/0004-3702(89)90077-5
   Web of Science ®Google Scholar
• Fauconnier, G., & Turner, M. (1998). Conceptual integration networks. Cognitive Science, 22(2), 133–187. doi: 10.1207/s15516709cog2202_1
   Web of Science ®Google Scholar
• Gentner, D., & Smith, L. A. (2013). Analogical learning and reasoning. In D. Reisberg (Ed.), The Oxford handbook of cognitive psychology (pp. 668–681). Oxford: Oxford University Press.
   Google Scholar
• Goguen, J. (2006). Mathematical models of cognitive space and time. Reasoning and cognition; proceedings of the interdisciplinary conference on reasoning and cognition (pp. 125–128). Tokyo: Keio University Press.
   Google Scholar
• Goguen, J. A., & Harrell, D. F. (2010). Style: A computational and conceptual blending-based approach. In S. Argamon, K. Burns, & S. Dubnov (Eds.), The structure of style (pp. 291–316). Heidelberg: Springer.
   Google Scholar
• Kutz, O., Bateman, J., Neuhaus, F., Mossakowski, T., & Bhatt, M. (2015). E pluribus unum. In T. R. Besold, M. Schorlemmer, & A. Smaill (Eds.), Computational creativity research: Towards creative machines (Vol. 7, pp. 167–196). Berlin: Atlantis Press.
   Google Scholar
• Kutz, O., Mossakowski, T., Hois, J., Bhatt, M., & Bateman, J. (2012). Ontological blending in DOL. Proceedings of the 1st international workshop on “computational creativity, concept invention, and general intelligence”. Osnabrück: Institute of Cognitive Science.
   Google Scholar
• Li, B., Zook, A., Davis, N., & Riedl, M. (2012). Goal-driven conceptual blending: A computational approach for creativity. Proceedings of the third international conference on computational creativity (pp. 9–16). ComputationalCreativity.net.
   Google Scholar
• Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. New York, NY: Henry Holt.
   Google Scholar
• Martinez, M., Besold, T. R., Abdel-Fattah, A., Gust, H., Schmidt, M., Krumnack, U., & Kühnberger, K.-U. (2012). Theory blending as a framework for creativity in systems for general intelligence. In P. Wang & B. Goertzel (Eds.), Theoretical foundations of artificial general intelligence (pp. 219–239). Berlin: Atlantis Press.
   Google Scholar
• Martinez, M., Krumnack, U., Smaill, A., Besold, T. R., Abdel-Fattah, A. M., Schmidt, M., & Pease, A. (2014). Algorithmic aspects of theory blending. In G. Aranda-Corral, J. Calmet, & F. Martín-Mateos (Eds.), Artificial intelligence and symbolic computation (Vol. 8884, pp. 180–192). Cham: Springer.
   Google Scholar
• Mossakowski, T., Krumnack, U., & Maibaum, T. (2015). What is a derived signature morphism? In M. Codescu, R. Diaconescu, and I. Tutu (Eds.), Recent trends in algebraic development techniques: 22nd international workshop, WADT 2014, Sinaia, September 4–7, 2014, Revised selected papers (pp. 90–109). Springer.
   Google Scholar
• Ontañón, S., & Plaza, E. (2010). Amalgams: A formal approach for combining multiple case solutions. In I. Bichindaritz & S. Montani (Eds.), Case-based reasoning: Research and development (Vol. 6176, pp. 257–271). Heidelberg: Springer.
   Google Scholar
• Ontañón, S., & Plaza, E. (2012). On knowledge transfer in case-based inference. In B. D. Agudo & I. Watson (Eds.), Case-based reasoning research and development (Vol. 7466, pp. 312–326). Berlin: Springer.
   Google Scholar
• Pereira, F. C. (2007). Creativity and AI: A conceptual blending approach. Berlin: Mouton de Gruyter.
   Google Scholar
• Pereira, F. C., & Cardoso, A. (2003). Optimality principles for conceptual blending: A first computational approach. AISB Journal, 1(4), 351–369.
   Google Scholar
• Plotkin, G. D. (1970). A note on inductive generalization. Machine Intelligence, 5, 153–163.
   Google Scholar
• Rosch, E. (1988). Principles of categorization. In A. Collins & E. E. Smith (Eds.), Readings in cognitive science, a perspective from psychology and artificial intelligence (pp. 312–322). San Mateo, CA: Morgan Kaufmann.
   Google Scholar
• Schmidt, M., Krumnack, U., Gust, H., & Kühnberger, K.-U. (2014). Heuristic-driven theory projection: An overview. In H. Prade & G. Richard (Eds.), Computational approaches to analogical reasoning: Current trends (pp. 163–194). Berlin: Springer.
   Google Scholar
• Schorlemmer, M., Confalonieri, R., & Plaza, E. (2016). Coherent concept invention. Proceedings of the workshop on computational creativity, concept invention, and general intelligence (C3GI 2016). CEUR-WS.org.
   Google Scholar
• Schwering, A., Krumnack, U., Kühnberger, K.-U., & Gust, H. (2009). Syntactic principles of heuristic-driven theory projection. Journal of Cognitive Systems Research, 10(3), 251–269. doi: 10.1016/j.cogsys.2008.09.002
   Web of Science ®Google Scholar
• Thagard, P., & Stewart, T. C. (2011). The aha! experience: Creativity through emergent binding in neural networks. Cognitive Science, 35(1), 1–33. doi: 10.1111/j.1551-6709.2010.01142.x
   PubMed Web of Science ®Google Scholar
• Veale, T., & O'Donoghue, D. (2000). Computation and blending. Cognitive Linguistics, 11(3/4), 253–281.
   Web of Science ®Google Scholar