Imagining Imagination: A Computational Framework Using Associative Memory Models and Vector Space Models: Unterschied zwischen den Versionen
(Die Seite wurde neu angelegt: „== Reference == Derrall Heath, Aaron Dennis and Dan Ventura: Imagining Imagination: A Computational Framework Using Associative Memory Models and Vector Spac…“) |
(→Bibtex) |
||
(2 dazwischenliegende Versionen desselben Benutzers werden nicht angezeigt) | |||
Zeile 33: | Zeile 33: | ||
location = {Park City, Utah, USA}, | location = {Park City, Utah, USA}, | ||
pages = {244-251}, | pages = {244-251}, | ||
− | url = {http://computationalcreativity.net/iccc2015/proceedings/11_1Heath.pdf | + | url = {http://computationalcreativity.net/iccc2015/proceedings/11_1Heath.pdf http://de.evo-art.org/index.php?title=Imagining_Imagination:_A_Computational_Framework_Using_Associative_Memory_Models_and_Vector_Space_Models }, |
− | |||
publisher = {International Association for Computational Creativity}, | publisher = {International Association for Computational Creativity}, | ||
keywords = {computational, creativity}, | keywords = {computational, creativity}, |
Aktuelle Version vom 2. November 2015, 21:05 Uhr
Inhaltsverzeichnis
Reference
Derrall Heath, Aaron Dennis and Dan Ventura: Imagining Imagination: A Computational Framework Using Associative Memory Models and Vector Space Models. In: Computational Creativity 2015 ICCC 2015, 244-251.
DOI
Abstract
Imagination is considered an important component of the creative process, and many psychologists agree that imagination is based on our perceptions, experiences, and conceptual knowledge, recombining them into novel ideas and impressions never before experienced. As an attempt to model this account of imagination, we introduce the Associative Conceptual Imagination (ACI) framework that uses associative memory models in conjunction with vector space models. ACI is a framework for learning conceptual knowledge and then learning associations between those concepts and artifacts, which facilitates imagining and then creating new and interesting artifacts. We discuss the implications of this framework, its creative potential, and possible ways to implement it in practice. We then demonstrate an initial prototype that can imagine and then generate simple images.
Extended Abstract
Bibtex
@inproceedings{ author = {Heath, Derrall and Dennis, Aaron and Ventura, Dan}, title = {Imagining Imagination: A Computational Framework Using Associative Memory Models and Vector Space Models}, booktitle = {Proceedings of the Sixth International Conference on Computational Creativity}, series = {ICCC2015}, year = {2015}, month = {Jun}, location = {Park City, Utah, USA}, pages = {244-251}, url = {http://computationalcreativity.net/iccc2015/proceedings/11_1Heath.pdf http://de.evo-art.org/index.php?title=Imagining_Imagination:_A_Computational_Framework_Using_Associative_Memory_Models_and_Vector_Space_Models }, publisher = {International Association for Computational Creativity}, keywords = {computational, creativity}, }
Used References
Barsalou, L. W. 1999. Perceptions of perceptual symbols. Behavioral and Brain Sciences 22(04):637–660.
Baydin, A. G.; de M´antaras, R. L.; and Onta˜n´on, S. 2014. A semantic network-based evolutionary algorithm for computational creativity. Evolutionary Intelligence 8(1):3–21.
Beaney, M. 2005. Imagination and Creativity. Open University Milton Keynes, UK.
Bhatia, S., and Chalup, S. K. 2013. A model of heteroassociative memory: Deciphering surprising features and locations. In Proceedings of the 4th International Conference on Computational Creativity, 139–146.
Breault, V.; Ouellet, S.; Somers, S.; and Davies, J. 2013. SOILIE: A computational model of 2d visual imagination. In Proceedings of the 12th International Conference on Cognitive Modeling, 95–100.
Colton, S. 2008. Creativity versus the perception of creativity in computational systems. Creative Intelligent Systems: Papers from the AAAI Spring Symposium 14–20.
Cs´ıkzentmih´alyi, M., and Robinson, R. E. 1990. The Art of Seeing. The J. Paul Getty Trust Office of Publications.
Currie, G., and Ravenscroft, I. 2002. Recreative Minds: Imagination in Philosophy and Psychology. Oxford University Press.
De Smedt, T. 2013. Modeling Creativity: Case Studies in Python. University Press Antwerp.
Denhi`ere, G., and Lemaire, B. 2004. A computational model of children’s semantic memory. In Proceedings of the 26th Conference of the Cognitive Science Society, 297–302. Mahwah, NJ: Lawrence Erlbaum Associates.
DiPaola, S., and Gabora, L. 2009. Incorporating characteristics of human creativity into an evolutionary art algorithm. Genetic Programming and Evolvable Machines 10(2):97– 110.
Fauconnier, G., and Turner, M. 1998. Conceptual integration networks. Cognitive Science 22(2):133–187.
Frome, A.; Corrado, G.; Shlens, J.; Bengio, S.; Dean, J.; Ranzato, M.; and Mikolov, T. 2013. DeViSE: A deep visualsemantic embedding model. In Advances In Neural Information Processing Systems, 2121–2129.
Gabora, L., and Ranjan, A. 2013. How Insight Emerges in Distributed, Content-addressable Memory. Oxford University Press.
Gaut, B. 2003. Creativity and imagination. The Creation of Art 148–173.
Gens, R., and Domingos, P. 2013. Learning the structure of sum-product networks. In Proceedings of the 30th International Conference on Machine Learning, volume 28, 873–880.
Gero, J. S. 1996. Creativity, emergence, and evolution in design. Knowledge-Based Systems 9:435–448.
Harrell, D. F. 2005. Shades of computational evocation and meaning: The GRIOT system and improvisational poetry generation. In Proceedings of the Sixth Digital Arts and Culture Conference, 133–143.
Heath, D.; Norton, D.; and Ventura, D. 2014. Conveying semantics through visual metaphor. ACM Transactions on Intelligent Systems and Technology 5(2):31:1–31:17.
Hinton, G.; Osindero, S.; and Teh, Y.-W. 2006. A fast learning algorithm for deep belief nets. Neural Computation 18(7):1527–1554.
Kosko, B. 1988. Bidirectional associative memories. IEEE Transactions on Systems, Man and Cybernetics 18(1):49– 60.
Krzeczkowska, A.; El-Hage, J.; Colton, S.; and Clark, S. 2010. Automated collage generation—with intent. In Proceedings of the 1st International Conference on Computational Creativity, 36–40.
Lake, B. M.; Salakhutdinov, R. R.; and Tenenbaum, J. 2013. One-shot learning by inverting a compositional causal process. In Advances in Neural Information Processing Systems, 2526–2534.
Landauer, T., and Dumais, S. 1997. A solution to Plato’s problem: The latent semantic analysis theory of acquisition induction and representation of knowledge. Psychological Review 104(2):211–240.
Machado, P.; Romero, J.; and Manaris, B. 2007. Experiments in computational aesthetics: An iterative approach to stylistic change in evolutionary art. In Romero, J., and Machado, P., eds., The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Berlin: Springer. 381– 415.
Martinez, M.; Besold, T.; Abdel-Fattah, A.; Kuehnberger, K.-U.; Gust, H.; Schmidt, M.; and Krumnack, U. 2011. Towards a domain-independent computational framework for theory blending. In 2011 AAAI Fall Symposium Series.
McGregor, S.;Wiggins, G.; and Purver, M. 2014. Computational creativity: A philosophical approach, and an approach to philosophy. In Proceedings of the 5th International Conference on Computational Creativity, 254–262.
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013. Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations.
Miranda, E. R., and Biles, A. 2007. Evolutionary Computer Music. Springer.
Norton, D.; Heath, D.; and Ventura, D. 2013. Finding creativity in an artificial artist. Journal of Creative Behavior 47(2):106–124.
Permar, J., and Magerko, B. 2013. A conceptual blending approach to the generation of cognitive scripts for interactive narrative. In Proceedings of the Ninth Artificial Intelligence and Interactive Digital Entertainment Conference, 44–50.
Poon, H., and Domingos, P. 2011. Sum-product networks: A new deep architecture. In Proceedings of the Twenty-Seventh Annual Conference on Uncertainty in Artificial Intelligence, 337–346. AUAI Press.
Rapp, R. 2003. Word sense discovery based on sense descriptor dissimilarity. In Proceedings of the Ninth Machine Translation Summit, 315–322.
Salton, G. 1971. The SMART Retrieval System— Experiments in Automatic Document Processing. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.
Steinbr¨uck, A. 2013. Conceptual blending for the visual domain. Master’s thesis, University of Amsterdam.
Stevenson, L. F. 2003. Twelve conceptions of imagination. British Journal of Aesthetics 43(3):238–59.
Todd, P. M. 1992. A connectionist system for exploring melody space. In Proceedings of the International Computer Music Conference, 65–68. International Computer Music Association.
Turney, P. D., and Pantel, P. 2010. From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research 37:141–188.
Turney, P. D. 2006. Similarity of semantic relations. Computational Linguistics 32(3):379–416.
Veale, T. 2012. From conceptual mash-ups to bad-ass blends: A robust computational model of conceptual blending. In Proceedings of the 3rd International Conference on Computational Creativity, 1–8.
Vygotsky, L. 2004. Imagination and Creativity in Childhood. Journal of Russian and East European Psychology 42(1):7–97.
Zhu, J., and Harrell, D. F. 2008. Daydreaming with intention: Scalable blending-based imagining and agency in generative interactive narrative. In AAAI Spring Symposium: Creative Intelligent Systems, volume 156.
Links
Full Text
http://computationalcreativity.net/iccc2015/proceedings/11_1Heath.pdf