The scenario is pretty simple, and I'm sure it's been done a million times. The problem is I don't know the terminology to find the correct resources on the web.
Scenario: I have an environment that can be described in terms of 5 parameters, including and input value A and an output variable B. There is a dataset containing 100 rows and values for each parameter.
The output B depends on A as well as the remaining environmental variables.
The goal is to find the best value for input A such that output B is minimized.
What does the solution for this problem look like? Is it Machine learning, neural networks, a mathematical optimization problem? How is this best approached?
Extension: if I didn't have a dataset in practice, how would I train a system to suggest different values for A until a minimum is reached? Can neural networks be applied here? Or are we talking about a looping procedure that does know maths in each operation until the output doesn't change much anymore?
I thought the generalization would make it more difficult to answer. What I am describing is a number of temperature/humidity measure measurements for both inside my house and outside. The input that I can control is the fan speed setting on my evaporative aircon and the output is the lounge temperature which I want to minimize.
During sample gathering, I don't care much about the output. The set of 100 values was arbitrary and more (and diverse) samples can be obtained.
The range of A is a fan speed with discrete values 1-6. Humidity is a percentage and temperature is 20-45 degrees.