Computational Creativity Theory: The FACE and IDEA Descriptive Models
Inhaltsverzeichnis
Reference
Simon Colton, John Charnley, and Alison Pease: Computational Creativity Theory: The FACE and IDEA Descriptive Models. In: Computational Creativity 2011 ICCC 2011, pp. 90-95.
DOI
Abstract
We introduce computational creativity theory (CCT) as an analogue in computational creativity research to computational learning theory in machine learning. In its current draft, CCT comprises the FACE descriptive model of creative acts as tuples of generative acts, and the IDEA descriptive model of the impact such creative acts may have. To introduce these, we simplify various assumptions about software development, background material given to software, how creative acts are per- formed by computer, and how audiences consume the results. We use the two descriptive models to perform two comparisons studies, firstly for mathematical dis- covery software, and secondly for visual art generating programs. We conclude by discussing possible addi- tions, improvements and refinements to CCT.
Extended Abstract
Bibtex
@inproceedings{
author = {Simon Colton, John Charnley, and Alison Pease},
title = {Computational Creativity Theory: The FACE and IDEA Descriptive Models},
editor = {Dan Ventura, Pablo Gervás, D. Fox Harrell, Mary Lou Maher, Alison Pease and Geraint Wiggins},
booktitle = {Proceedings of the Second International Conference on Computational Creativity},
series = {ICCC2011},
year = {2011},
month = {April},
location = {México City, México},
pages = {90-95},
url = {http://iccc11.cua.uam.mx/proceedings/the_foundational/colton_1_iccc11.pdf, http://de.evo-art.org/index.php?title=Computational_Creativity_Theory:_The_FACE_and_IDEA_Descriptive_Models },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},
}
Used References
Angluin, D. 1992. Computational learning theory: Survey and selected bibliography. Proc. 24th ACM symp. on Theory of Comp..
Boden, M. 2003. The Creative Mind: Myths and Mechanisms (second edition). Routledge.
Buchanan, B. 2001. Creativity at the meta-level. AI Mag. 22(3).
Cardoso, A.; Veale, T.; and Wiggins, G. 2009. Converging on the divergent: The history (and future) of the international joint workshops in computational creativity. AI Magazine 30(3).
Colton, S., and Pease, A. 2005. The TM system for repairing non-theorems. ENTCS, 125(3).
Colton, S.; Pease, A.; and Ritchie, G. 2001. The effect of input knowledge on creativity. Proc. ICCBR’01 Wshop on Creat. Syst..
Colton, S. 2001. Experiments in meta-theory formation. In Proc. of the AISB’01 Symposium on AI and Creativity in Arts and Science.
Colton, S. 2002. Auto. Theory Formation in Pure Maths. Springer.
Colton, S. 2008a. Automatic invention of fitness functions, with application to scene generation. In Proc. of the EvoMusArt Wshop.
Colton, S. 2008. Creativity vs the perception of creativity in com- putational systems. Proc. AAAI Spring Symp. on Creative Systems.
Colton, S. 2009. Seven catchy phrases for computational creativity research. In Proceedings of the Dagstuhl Seminar: Computational Creativity: An Interdisciplinary Approach.
Hull, M., and Colton, S. 2007. Towards a general framework for program generation in creative domains. In Proceedings of the 4th International Joint Workshop on Computational Creativity.
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.
Lenat, D. 1982. AM: Discovery in mathematics as heuristic search. In Knowledge-Based Systems in AI. McGraw-Hill.
Macedo, L., and Cardoso, A. 2002. Assessing creativity: the im- portance of unexpected novelty. Proc. ECAI Wshop on Creat. Syst..
Machado, P., and Cardoso, A. 2000. NEvAr – the assessment of an evolutionary art tool. In Proc. of the AISB Symp. on Creative and Cultural Aspects and Applications of AI and Cognitive Science.
McCorduck, P. 1991. AARON’s Code: Meta-Art, Artificial Intelligence, and the Work of Harold Cohen. W.H. Freeman and Co. Mitchell, T. 1997. Machine Learning. McGraw Hill.
Pease, A. 2007. A Computational Model of Lakatos-style Reason- ing. Ph.D., School of Informatics, University of Edinburgh.
Pease, A., and Colton, S. 2011a. CCT: Inspirations behind the FACE and IDEA models In Proc. of the Int. Conf. on Comp. Creat.
Pease, A., and Colton, S. 2011. On impact and evaluation in com- putational creativity In Proc. of the AISB symp. on AI and Philos.
Ritchie, G. 2007. Some empirical criteria for attributing creativity to a computer program. Minds and Machines 17.
Schapire, R. 1990. Strength of weak learnability. Mach. Learn. 5. Sloman, A. 1978. The Computer Revolution in Philosophy. The Harvester Press.
Thagard, P. 1993. Comp. Philosophy of Science. MIT Press. Uzzi, B., and Spiro, J. 2005. Collaboration and creativity: The small world problem. Am. Journal of Sociology 111(2).
Valiant, L. 1984. A theory of the learnable. Comm. ACM 27(11).
Wiggins, G. 2006. Searching for computational creativity. New Generation Computing 24(3).
Wundt, W. 1874. Grundzuge der Phvsiologischen Psychologie. Engelmann.
Links
Full Text
http://iccc11.cua.uam.mx/proceedings/the_foundational/colton_1_iccc11.pdf
