Knowledge Discovery in Chess Using an Aesthetics Approach

Abstract

Computational aesthetics is a relatively new subfield of artificial intelligence (AI). It includes research that enables computers to “recognize” (and evaluate) beauty in various domains such as visual art, music, and games. Aside from the benefit this gives to humans in terms of creating and appreciating art in these domains, there are perhaps also philosophical implications about the nature and “mechanics” of aesthetic perception in humans. We can, potentially, learn more about ourselves as we replicate or simulate this ability in machines. My original intention or objective was to develop an aesthetics model or method for computers to recognize beauty in the game of chess since it is greatly appreciated by human players1—there is even an entire subdomain devoted to it in the form of chess compositions2—and because it has other promising applications, such as improving computer chess heuristics, for example, in solving complex chess problems. My intention was also that this model should correlate positively and reasonably well with human player aesthetic assessment. Both were achieved with better experimentally validated success than previous approaches.3 A computer program called CHESTHETICA (see appendix) was developed to aid in the complex calculations involved. It can identify beautiful combinations4 in large databases of chess games based on the aesthetics model.5