Automated Generation of Cross-Domain Analogies via Evolutionary Computation
Inhaltsverzeichnis
Reference
Atilim Gunes Baydin, Ramon Lopez De Mantaras and Santiago Ontanon: Automated Generation of Cross-Domain Analogies via Evolutionary Computation. In: Computational Creativity 2012 ICCC 2012, 25-32.
DOI
Abstract
Analogy plays an important role in creativity, and is extensively used in science as well as art. In this pa- per we introduce a technique for the automated gener- ation of cross-domain analogies based on a novel evo- lutionary algorithm (EA). Unlike existing work in com- putational analogy-making restricted to creating analo- gies between two given cases, our approach, for a given case, is capable of creating an analogy along with the novel analogous case itself. Our algorithm is based on the concept of “memes”, which are units of culture, or knowledge, undergoing variation and selection un- der a fitness measure, and represents evolving pieces of knowledge as semantic networks. Using a fitness function based on Gentner’s structure mapping theory of analogies, we demonstrate the feasibility of sponta- neously generating semantic networks that are analo- gous to a given base network.
Extended Abstract
Bibtex
@inproceedings{
author = {Atilim Gunes Baydin, Ramon Lopez De Mantaras and Santiago Ontanon},
title = {Automated Generation of Cross-Domain Analogies via Evolutionary Computation},
editor = {Mary Lou Maher, Kristian Hammond, Alison Pease, Rafael Pérez y Pérez, Dan Ventura and Geraint Wiggins},
booktitle = {Proceedings of the Third International Conference on Computational Creativity},
series = {ICCC2012},
year = {2012},
month = {May},
location = {Dublin, Ireland},
pages = {25-32},
url = {http://computationalcreativity.net/iccc2012/wp-content/uploads/2012/05/025-Baydin.pdf, http://de.evo-art.org/index.php?title=Automated_Generation_of_Cross-Domain_Analogies_via_Evolutionary_Computation },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},
}
Used References
Bickhard, M. H., and Campbell, D. T. 2003. Variations in variation and selection: the ubiquity of the variation-and- selective-retention ratchet in emergent ogranizational com- plexity. Foundations of Science 8:215–2182.
Boden, M. A. 2004. The Creative Mind: Myths and Mech- anisms. London: Routledge, second edition.
Boden, M. A. 2009. Computer models of creativity. AI Magazine 30(3):23–34.
Clement, J. 1988. Observed methods for generating analogies in scientific problem solving. Cognitive Science 12:563–586.
Coello Coello, C. A.; Lamont, G. B.; and Van Veldhuizen, D. A. 2007. Evolutionary Algorithms for Solving Multi- Objective Problems. Springer.
Dawkins, R. 1989. The Selfish Gene. Oxford University Press.
Dennett, D. C. 1995. Darwin’s Dangerous Idea: Evolution and the Meanings of Life. Simon & Schuster.
Falkenhainer, B.; Forbus, K. D.; and Gentner, D. 1989. The Structure-Mapping Engine: Algorithm and examples. Arti- ficial Intelligence 41:1–63.
Fauconnier, G., and Mark, T. 2002. The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York: Basic Books.
Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. MIT Press.
French, R. M. 2002. The computational modeling of analogy-making. Trends in Cognitive Sciences 6(5):200– 205.
Gabora, L. 1997. The origin and evolution of culture and creativity. Journal of Memetics – Evolutionary Models of Transmission 1.
Gentner, D., and Markman, A. B. 1997. Structure mapping in analogy and similarity. American Psychologist 52:45–56.
Gil-White, F. 2008. Let the meme be (a meme): insisting too much on the genetic analogy will turn it into a straight- jacket. In Botz-Bornstein, T., ed., Culture, Nature, Memes. Newcastle upon Tyne: Cambridge Scholars.
Havasi, C.; Speer, R.; and Alonso, J. 2007. ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge. In Proceedings of Recent Advances in Natural Language Processing.
Hofstadter, D. R. 1995. Fluid concepts and creative analo- gies: Computer models of the fundamental mechanisms of thought. New York: Basic Books.
Hofstadter, D. 2001. Analogy as the core of cognition. In Gentner, D.; Holyoak, K. J.; and Kokinov, B., eds., Analogi- cal Mind: Perspectives From Cognitive Science. Cambridge, MA: MIT Press. 499–538.
Holyoak, K. J., and Thagard, P. 1996. Mental Leaps: Anal- ogy in Creative Thought. Bradford Books.
Koza, J. R.; Keane, M. A.; Streeter, M. J.; Mydlowec, W.; Yu, J.; and Lanza, G. 2003. Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers.
Montes, H. A., and Wyatt, J. L. 2004. Graph representa- tion for program evolution: An overview. Technical report, University of Birmingham School of Computer Science.
Moscato, P. A.; Cotta, C.; and Mendes, A. 2004. Stud- ies in Fuzziness and Soft Computing – New Optimization Techniques in Engineering. New York: Springer. chapter Memetic Algorithms.
Mueller, E. T. 2006. Commonsense Reasoning. Morgan Kaufmann.
O’Donoghue, D. 2004. Finding Novel Analogies. Ph.D. Dis- sertation, University College Dublin, Department of Com- puter Science, Dublin, Ireland.
Pereira, F. C. 2007. Creativity and Artificial Intelligence: A Conceptual Blending Approach. New York: Mouton de Gruyter.
Romero, J. J., and Machado, P. 2008. The Art of Artifi- cial Evolution: A Handbook on Evolutionary Art and Music. Springer.
Simonton, D. K. 2003. Scientific creativity as constrained stochastic behavior: The integration of product, person, and process perspectives. Psychological Bulletin 129(4):475– 494.
Sowa, J. F. 1991. Principles of Semantic Networks: Ex- plorations in the Representation of Knowledge. San Mateo: Mogran Kaufmann.
Thagard, P.; Holyoak, K. J.; Nelson, G.; and Gochfeld, D. 1990. Analog retrieval by constraint satisfaction. Artificial Intelligence 46:259–310.
Veale, T., and Keane, M. 1997. The competence of sub- optimal structure mapping on hard analogies. In Proceed- ings of the 15th International Joint Conference on AI. San Mateo, CA: Morgan Kauffman.
Ward, T. B.; Smith, S. M.; and Vaid, J. 2001. Creative Thought: An Investigation of Conceptual Structures and Processes. Washington, DC: American Psychological As- sociation.
Links
Full Text
http://computationalcreativity.net/iccc2012/wp-content/uploads/2012/05/025-Baydin.pdf
