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11 - Artificial Intelligence and Imagination

from Part I - Theoretical Perspectives on the Imagination

Published online by Cambridge University Press:  26 May 2020

By
Jim Davies
Edited by
Anna Abraham
Anna Abraham
Affiliation:
University of Georgia
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Summary

There is a long history of software programs that implement creativity, imagery, and imagination. They are used for generation of entertainment, creation of art, and simulation of the world. Some of these programs are created to shed light on the nature of imagination, and some are explicitly designed to model imagination in humans. These AIs also display a range of domain areas, from the creation of paintings, music, and plot structures, to the generation of entire fictional worlds. AI aids our understanding of imagination in several ways: It provides ideas of how it could work, it tests the feasibility of theories, and brings to light assumptions and new problems that are difficult to generate from theories that are only described in words.

Keywords

Artartificial intelligenceimaginationimageryprocedural generationgenerative art
Type
Chapter
Information
The Cambridge Handbook of the Imagination , pp. 162 - 172
Publisher: Cambridge University Press
Print publication year: 2020

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References

de la Re, A., Abad, F., Camahort, E., and Juan, M. C. (2009). Tools for Procedural Generation of Plants in Virtual Scenes. In International Conference on Computational Science. Berlin, Germany: Springer, 801–810.Google Scholar
Aitken, M., Butler, G., Lemmon, D., Saindon, E., Peters, D., and Williams, G. (2004). The Lord of the Rings: The Visual Effects that Brought Middle Earth to the Screen. Proceedings of the Conference on SIGGRAPH 2004 Course Notes – GRAPH ’04, 11-es. doi.org/10.1145/1103900.1103911.Google Scholar
Bernardes, G., and Cocharro, D. (2019). Dynamic Music Generation, Audio Analysis-Synthesis Methods. In Lee, N (ed.), Encyclopedia of Computer Graphics and Games, Berlin, Germany: Springer.Google Scholar
Boden, M. A., and Edmonds, E. A. (2009). What is Generative Art? Digital Creativity, 20(1–2), 21–46.CrossRefGoogle Scholar
Bogart, B. D., Pasquier, P., and Barnes, S. J. (2013). An Integrative Theory of Visual Mentation and Spontaneous Creativity. Proceedings of the 9th ACM Conference on Creativity & Cognition – C&C ’13, 264. doi.org/10.1145/2466627.2466639.CrossRefGoogle Scholar
Breault, V., Ouellet, S., Somers, S., and Davies, J. (2013). SOILIE: A Computational Model of 2D Imagination. In West, R and Stewart, T (eds.), Proceedings of the 12th International Conference on Cognitive Modeling, Ottawa, Canada: Carleton University, 95–100.Google Scholar
Cohen, H. (1995). The Further Exploits of AARON, Painter. Stanford Humanities Review, 4(2), 141–158.Google Scholar
Coyne, B., and Sproat, R. (2001). WordsEye: An Automatic Text-to-Scene Conversion System. In Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. Sydney, Australia: Association for Computing Machinery, 487–496.Google Scholar
Davies, J., and Francis, A. G. (2013). The Role of Artificial Intelligence Research Methods in Cognitive Science. In West, R and Stewart, T (eds.), Proceedings of the 12th International Conference on Cognitive Modeling, Ottawa, Canada: Carleton University, 439–444.Google Scholar
De Mantaras, R. L., and Arcos, J. L. (2002). AI and Music: From Composition to Expressive Performance. AI Magazine, 23(3), 43–43.Google Scholar
Ebert, D. S., Musgrave, F. K., Peachey, D., Perline, K., and Worey, S. (2003). Texturing & Modeling: A Procedural Approach. 3rd edition. New York, NY: Morgan Kaufmann.Google Scholar
Eslami, S. A., Rezende, D. J., Besse, F., et al. (2018). Neural Scene Representation and Rendering. Science, 360 (6394), 1204–1210.Google Scholar
Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., and Hanrahan, P. (2012). Example-Based Synthesis of 3D Object Arrangements. ACM Transactions on Graphics – Proceedings of ACM SIGGRAPH Asia 2012. 31(6), article 135.Google Scholar
Gaisbauer, W., and Hlavacs, H. (2017). Procedural Attack! Procedural Generation for Populated Virtual Cities: A Survey. International Journal of Serious Games, 4(2).Google Scholar
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative Adversarial Nets. In Advances in Neural Information Processing Systems. 27, 2672–2680.Google Scholar
Hammes, J. (2001). Modeling of Ecosystems as a Data Source for Real-Time Terrain Rendering. In Digital Earth Moving. Berlin, Germany: Springer, 98–111.Google Scholar
Heath, D., Dennis, A. W., and Ventura, D. (2015). Imagining Imagination: A Computational Framework Using Associative Memory Models and Vector Space Models. In Proceedings of the Sixth International Conference on Computational Creativity, 244–251.Google Scholar
Hendrikx, M., and Meijer, S (2011). Procedural Content Generation for Games: A Survey. ACM Trans.Multimedia Comput. Commun. Appl., 1, February, 1–24.Google Scholar
Hiller, L. A. Jr., and Isaacson, L. M. (1958). Musical Composition with a High-Speed Digital Computer. Journal of the Audio Engineering Society, 6(3), 154–160.Google Scholar
Johnstone, K. (1979). Impro: Improvisation and the Theatre. London, UK: Faber and Faber.Google Scholar
Kato, H., and Harada, T. (2014). Image Reconstruction from Bag-of-Visual-Words. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 955–962.Google Scholar
Kelly, G., and McCabe, H. (2006). A Survey of Procedural Techniques for City Generation. The ITB Journal, 7(2), 5.Google Scholar
Kosslyn, S. M., and Shwartz, S. P. (1977). A Simulation of Visual Imagery. Cognitive Science 1, 265–295.Google Scholar
Liang, X., Zhuo, B., Li, P., and He, L. (2016). CNN Based Texture Synthesize with Semantic Segment. arxiv.org/abs/1605.04731.Google Scholar
McCorduck, P. (1991). Aaron’s Code: Meta-Art, Artificial Intelligence, and the Work of Harold Cohen. New York, NY: Macmillan.Google Scholar
Meuller, P., Wonka, P., Haegler, S., Ulmer, A., and van Gul, L. (2006). Procedural Generation of Buildings. ACM Transactions on Graphics, 25(3), 614–623.Google Scholar
Morris, C. (2011). World of Warcraft Maker Turns 20, Looks Ahead. Plugged In. games.yahoo.com/blogs/plugged-in/world-warcraft-maker-turns-20-looks-ahead-450.html.Google Scholar
Parish, Y. I. H., and Mueller, P. (2001). Procedural Modeling of Cities. In Proceedings of ACM SIGGRAPH 2001. New York, NY: ACM Press/ ACM SIGGRAPH, 301–308.Google Scholar
Pascanu, R., Li, Y., Vinyals, O., et al. (2017). Learning Model-Based Planning from Scratch. arxiv.org/abs/1707.06170.Google Scholar
Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature, 529(7587), 484.Google Scholar
Silver, D., Hubert, T., Schrittwieser, J., et al. (2018). A General Reinforcement Learning Algorithm that Masters Chess, Shogi, and Go Through Self-Play. Science, 362(6419), 1140–1144.Google Scholar
Thorn, B. (2001). BoB: An Improvisational Music Companion. PhD Dissertation, Computer Science Department. Pittsburgh, PA: Carnegie Mellon University, 281.Google Scholar
Veale, T. (2017). Déjà Vu All Over Again: On the Creative Value of Familiar Elements in the Telling of Original Tales. In Proceedings of ICCC 2017, the 8th International Conference on Computational Creativity, Atlanta, Georgia.Google Scholar
Weiner, J. (2011). “Where do Dwarf-Eating Carp Come From?” The New York Times Magazine. July 21.Google Scholar
Whitelaw, M. (2004). Metacreation: Art and Artificial Life. London: MIT Press.Google Scholar

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