"Relearning" parameters If this is a duplicate, I apologize. I'm not really sure what to even search for to try and find a duplicate/answer!

We are working on a system for providing musical feedback to change the 'mood' of a subject. There are a number of parameters of the music that we can manipulate.

At the same time, we measure the physiology of the subject. I'm using a dynamic Bayesian network to reasonably accurately determine the level of frustration of the subject.

I'm looking for a way to, based on the level of frustration, tell the music generation mechanism "this set of parameters is effective", or not. The music parameters would then adjust automatically and iteratively, as we continue to make judgements of the subject's level of frustration.

I'd appreciate any pointers I can get! Please let me know if I can provide any clarification!
This can be thought of as an active learning problem. You want feedback from the learning process to influence what kind of input examples you next train on.  Active learning is still a difficult problem in most settings. 

As @cohoz points out, hill climbing is an intuitive option in this case, but only if you always want to increase or decrease your measure of frustration. If instead the goal is to explore frustration as a function of as much of the music parameter feature space as possible, you will want to read about active learning and uncertainty.