abstract = "Researchers wishing to create computational systems
that themselves generate artworks face two interacting
challenges. The first is that the standards by which
artistic output is judged are notoriously difficult to
quantify. The larger AI community is currently involved
in a rich internal dialogue on methodological issues,
standards, and rigor, and hence murkiness with regard
to the assessment of output must be faced squarely. The
second challenge is that any artwork exists within an
extraordinarily rich cultural and historical context,
and it is rare that an artist who is ignorant of this
context will produce acceptable works. In this paper we
assert that these considerations argue for case-based
AI/Art systems that take critical criteria as
parameters. We describe an example system that produces
new bebop jazz melodies from a case-base of melodies,
using genetic programming techniques and a fitness
function based on user-provided critical criteria. We
discuss the role that such techniques may play in
future work on AI and the arts",
notes = "Combines case-base of existing highly valued bebop
jazz melodies with GP to produce new music. The music
is the response part of a call/response pair, ie a
novel improvised response to existing music. Is able to
evalulate the fitness of GP solutions using an
automatic critic, but the authors are not pleased with
the final solution even though the automatic critic
is.