Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-01T02:12:41.423Z Has data issue: false hasContentIssue false
  • English
  • Français

Atoms of EVE′: A Bayesian basis for esthetic analysis of style in sketching

Published online by Cambridge University Press:  27 June 2006

KEVIN BURNS
KEVIN BURNS
Affiliation:
MITRE Corporation, Bedford, Massachusetts, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

At its root level, style is actually an esthetic agreement between people. The question is, how can esthetic agreements be modeled and measured in artificial intelligence? This paper offers a formal theory called EVE′ and applies it to a novel test bed of dynamic drawings that combine features of music and sketching. The theory provides mathematical measures of expectations, violations, and explanations, which are argued to be the atomic components of the esthetic experience. The approach employs Bayesian methods to extend information measures proposed in other research. In particular, it is shown that information theory is useful at an entropic level to measure expectations (E) of signals and violations (V) of expectations, but that Bayesian theory is needed at a semantic level to measure explanations (E′) of meaning for the signals. The entropic and semantic measures are then combined in further measures of tension and pleasure at an esthetic level that is actually style.

Keywords

Bayesian TheoryDrawingFlowFunInformation TheoryMusic
Type
Research Article
Information
AI EDAM , Volume 20 , Issue 3 , August 2006 , pp. 185 - 199
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Attneave, F. (1954). Some informational aspects of visual perception. Psychological Review, 61(2), 183–193.Google Scholar
Biederman, I. (1987). Recognition by components: a theory of human image understanding. Psychological Review, 94(2), 115–147.Google Scholar
Boden, M. (2004). The Creative Mind: Myths and Mechanisms. New York: Routledge.
Brennan, S.E. (1985). Caricature generator: the dynamic exaggeration of faces by computer. Leonardo, 18(3), 170–178.Google Scholar
Burns, K. (2001). Mental models of line drawings. Perception, 30(6), 1249–1261.Google Scholar
Burns, K. (2004). Creature double feature: on style and subject in the art of caricature. Proc. AAAI Fall Symp. Style and Meaning in Language, Music, Art and Design, pp. 7–14. AAAI Technical Report FS-04-07.
Butcher, S. (1955). Aristotle Poetics. New York: Dover.
Csikszentmihalyi, M. (1991). Flow: The Psychology of Optimal Experience. New York: Harper Collins.
Dehaene, S. (1997). The Number Sense: How the Mind Creates Mathematics. Oxford: Oxford University Press.
Do, E.Y.-L. (2002). Drawing marks, acts, and reacts: toward a computational sketching interface for architectural design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 16(2), 149–171.Google Scholar
Dubnov, S. (2006). Musical structure by information rate. Unpublished manuscript.
Dubnov, S., McAdams, S., & Reynolds, R. (2004). Predicting human reactions to music on the basis of similarity structure and information theoretic measures of sound signals. Proc. AAAI Fall Symp. Style and Meaning in Language, Music, Art and Design, pp. 37–40. AAAI Technical Report FS-04-07.
Dubnov, S., McAdams, S., & Reynolds, R. (in press). Structural and affective aspects of music from statistical audio signal analysis. Journal of the American Society for Information Science and Technology.
Feldman, J. & Singh, M. (2005). Information along contours and object boundaries. Psychological Review, 112(1), 243–252.Google Scholar
Forbus, K.D. (2004). Qualitative spatial reasoning about sketch maps. AI Magazine, Fall, 61–72.
Jupp, J. & Gero, J. (2003). Towards a computational analysis of style in architectural design. Proc. IJCAI Workshop on Computational Approaches to Style Analysis and Synthesis, pp. 1–10, Acapulco, Mexico.
Kirsch, J.L. & Kirsch, R.A. (1986). The structure of paintings: formal grammar and design. Environment and Planning B, 13(1), 163–176.Google Scholar
Knill, D. & Richards, W. (1996). Perception as Bayesian Inference. Cambridge: Cambridge University Press.
Koster, R. (2005). A Theory of Fun for Game Design. Scottsdale, AZ: Paraglyph Press.
Matisse, H. (1995). Drawings: Themes and Variations. New York: Dover.
McCorduck, P. (1991). Aaron's Code: Meta-Art, Artificial Intelligence and the Work of Harold Cohen. New York: W.H. Freeman.
Mithen, S. (1996). The Prehistory of the Mind: The Cognitive Origins of Art and Science. New York: Thames and Hudson.
Mokhtarian, F. & Mackworth, A. (1986). Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1), 34–43.Google Scholar
Oatley, K. & Johnson-Laird, P. (1987). Toward a cognitive theory of emotions. Cognition and Emotion, 1(1), 29–50.Google Scholar
Picard, R. (2000). Affective Computing. Cambridge, MA: MIT Press.
Ramachandran, V.S. & Hirstein, W. (1999). The science of art: a neurological theory of aesthetic experience. Journal of Consciousness Studies, 6(1), 15–51.Google Scholar
Resnikoff, H.L. (1985). The Illusion of Reality: Topics in Information Science. New York: Springer.
Rhodes, G., Brennan, S., & Carey, S. (1987). Identification and ratings of caricatures: implications for mental representations of faces. Cognitive Psychology, 19(3), 473–497.Google Scholar
Rhodes, G. & McLean, I.A. (1990). Distinctiveness and expertise effects with homogeneous stimuli: towards a model of configural coding. Perception, 19(4), 773–794.Google Scholar
Richards, W. (1988). Natural Computation. Cambridge, MA: MIT Press.
Richards, W., Dawson, B., & Whittington, D. (1988). Encoding shape by curvature extrema. In Natural Computation (Richards, W., Ed.), pp. 83–98. Cambridge, MA: MIT Press.
Rosch, E. (1978). Principles of categorization. In Cognition and Categorization (Rosch, E. & Lloyd, B.B., Eds.). Hillsdale, NJ: Erlbaum.
Salen, K. & Zimmerman, E. (2004). Rules of Play: Game Design Fundamentals. Cambridge, MA: MIT Press.
Shannon, C. & Weaver, W. (1949). The Mathematical Theory of Communication. Urbana, IL: University of Illinois Press.
Sosa, R. & Gero, J. (2006). A computational framework to investigate creativity and innovation in design. Manuscript submitted for publication.
Stiny, G. & Mitchell, W. (1978). The Palladian grammar. Environment and Planning B, 5(1), 5–18.Google Scholar
Wachtel, E. (1993). The first picture show: cinematic aspects of cave art. Leonardo, 26(2), 135–140.Google Scholar
Wickens, C.D. & Hollands, J.G. (2000). Engineering Psychology and Human Performance. New York: Prentice–Hall.
Yerkes, R. & Dodson, J. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18(3), 459–482.Google Scholar