Sequential Decision Making in Artificial Musical Intelligence

My main research motivation is to develop complete autonomous agents that interact with people socially. For an agent to be social with respect to humans, it needs to be able to parse and process the human cultural experience. That in itself gives rise to many fascinating learning problems. Music, as a general target domain, serves as an excellent testbed for these research ideas. Musical skills involve extremely advanced knowledge representation and problem solving tools. Creating agents that can interact richly with people in the music domain is a challenge that will advance social agents research and contribute important and broadly applicable AI knowledge. This belief is fueled not just by my background in computer science and artificial intelligence, but also by my deep passion for music as well as my extensive musical training. One key aspect of musical intelligence which hasn’t been sufficiently studied is that of sequential decision-making. My thesis strives to answer the following question: How can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and multiagent interaction in the context of music.

Authors

• Elad Liebman SparkCognition

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