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.