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<http://schema.org/description>
"The system presented here shows the feasibility of modeling the knowledge\ninvolved in a complex musical activity by integrating sub-symbolic and symbolic\nprocesses. This research focuses on the question of whether there is any\nadvantage in integrating a neural network together with a distributed\nartificial intelligence approach within the music domain. The primary purpose\nof our work is to design a model that describes the different aspects a user\nmight be interested in considering when involved in a musical activity. The\napproach we suggest in this work enables the musician to encode his knowledge,\nintuitions, and aesthetic taste into different modules. The system captures\nthese aspects by computing and applying three distinct functions: rules, fuzzy\nconcepts, and learning.\n  As a case study, we began experimenting with first species two-part\ncounterpoint melodies. We have developed a hybrid system composed of a\nconnectionist module and an agent-based module to combine the sub-symbolic and\nsymbolic levels to achieve this task. The technique presented here to represent\nmusical knowledge constitutes a new approach for composing polyphonic music."
<https://cdnjs.deepai.org/profile/jeffrey-s-rosenschein>