A Mixed Observability Markov Decision Process Model for Musical Pitch
This work addresses a domain-specific problem for researchers in AI and music, but it is incremental as it adapts an existing MOMDP framework to a new application area.
The paper tackles the problem of modeling intelligent agents' interaction with musical pitch environments by applying Mixed Observability Markov Decision Processes (MOMDPs), resulting in a behavioral model that conveniently supports decision-making in this context.
Partially observable Markov decision processes have been widely used to provide models for real-world decision making problems. In this paper, we will provide a method in which a slightly different version of them called Mixed observability Markov decision process, MOMDP, is going to join with our problem. Basically, we aim at offering a behavioural model for interaction of intelligent agents with musical pitch environment and we will show that how MOMDP can shed some light on building up a decision making model for musical pitch conveniently.