The DONALD TAVEL ARTS AND TECHNOLOGY RESEARCH CENTER will soon be commercially releasing version 1.0 of the Avatar Machine Learning Improvisational Assistant, developed by Jason Palamara and Scott Deal. The Avatar program is a machine-learning-enabled “choice engine” which provides a dynamically sensitive duet while listening to live vibraphone performances. The initial version is geared for use with a vibraphone, with additional instruments soon to follow. Using this system, the musician performs improvisations on the vibraphone while the software listens, closely following the vibraphone performance. The package employs a Markov-chain model culled from Scott Deal’s improvisations. This mindfile database allows the software to generate novel content based on Scott Deal’s style. While the Markov transition database provides note-to-note transitions, the AvatarPlayer makes use of this data in several ways. Throughout a performance, the AvatarPlayer cycles through five playback behaviors (favor repetition, favor novelty, favor four notes, favor chords, and favor phrases), all of which make use of the database differently.