section_path: Derivative forms of aesthetics/Computational approaches
section_name: Computational approaches
paragraph_text: Computational approaches
  
  Computational approaches to aesthetics emerged amid efforts to use computer science methods "to predict, convey, and evoke emotional response to a piece of art. In this field, aesthetics is not considered to be dependent on taste but is a matter of cognition, and, consequently, learning. In 1928, the mathematician George David Birkhoff created an aesthetic measure M = O / C {\displaystyle M=O/C} as the ratio of order to complexity.
  
  In the 1960s and 1970s, Max Bense, Abraham Moles and Frieder Nake were among the first to analyze links between aesthetics, information processing, and information theory. Max Bense, for example, built on Birkhoff's aesthetic measure and proposed a similar information theoretic measure M a ¨ = R / H {\displaystyle M_{\ddot {a}}=R/H}, where R {\displaystyle R} is the redundancy and H {\displaystyle H} the entropy, which assigns higher value to simpler artworks.
  
  In the 1990s, Jürgen Schmidhuber described an algorithmic theory of beauty. This theory takes the subjectivity of the observer into account and postulates that among several observations classified as comparable by a given subjective observer, the most aesthetically pleasing is the one that is encoded by the shortest description, following the direction of previous approaches. Schmidhuber's theory explicitly distinguishes between that which is beautiful and that which is interesting, stating that interestingness corresponds to the first derivative of subjectively perceived beauty. He supposes that every observer continually tries to improve the predictability and compressibility of their observations by identifying regularities like repetition, symmetry, and fractal self-similarity.
  
  Since about 2005, computer scientists have attempted to develop automated methods to infer aesthetic quality of images. Typically, these approaches follow a machine learning approach, where large numbers of manually rated photographs are used to "teach" a computer about what visual properties are of relevance to aesthetic quality. A study by Y. Li and C.J. Hu employed Birkhoff's measurement in their statistical learning approach where order and complexity of an image determined aesthetic value. The image complexity was computed using information theory while the order was determined using fractal compression. There is also the case of the Acquine engine, developed at Penn State University, that rates natural photographs uploaded by users.
  
  There have also been relatively successful attempts with regard to chess and music. Computational approaches have also been attempted in film making as demonstrated by a software model developed by Chitra Dorai and a group of researchers at the IBM T.J. Watson Research Center. The tool predicted aesthetics based on the values of narrative elements. A relation between Max Bense 's mathematical formulation of aesthetics in terms of "redundancy" and "complexity" and theories of musical anticipation was offered using the notion of Information Rate.