  The system presented here shows the feasibility of modeling the knowledge
involved in a complex musical activity by integrating sub-symbolic and symbolic
processes. This research focuses on the question of whether there is any
advantage in integrating a neural network together with a distributed
artificial intelligence approach within the music domain. The primary purpose
of our work is to design a model that describes the different aspects a user
might be interested in considering when involved in a musical activity. The
approach we suggest in this work enables the musician to encode his knowledge,
intuitions, and aesthetic taste into different modules. The system captures
these aspects by computing and applying three distinct functions: rules, fuzzy
concepts, and learning.
  As a case study, we began experimenting with first species two-part
counterpoint melodies. We have developed a hybrid system composed of a
connectionist module and an agent-based module to combine the sub-symbolic and
symbolic levels to achieve this task. The technique presented here to represent
musical knowledge constitutes a new approach for composing polyphonic music.
