Asking optimal questions to differentiate object in set I have a problem in mind and I am sure this is likely an area of active research, but am at a loss as to the correct terminology and thus unable to find any reference literature.  It is best explained by example: 

Say I have several objects, for example mahogany chairs.  For each of these chairs I know a number of attributes about them such as their weight, texture, colour, age, etc.

If you contact me inquiring about one of my chairs and tell me some incomplete information, e.g. "dark brown, 60's era chair"  I want to decide optimally what further information (if any) do I require from you to identiy the chair of interest, i.e. what questions do I need to ask to obtain the required attribute information.  Additionally, in the likely case that there are many questions I will need to ask you, how can I optimise the questions I ask you.  Optimisation could mean minimising the expected number of quesitons I need to ask you, or asking questions I think you are most likely to be able to answer.

So I would appreciate some information or references for this kind of problem.  It's worth noting I am interested in implementing this computationally (hence posting here) as opposed to a purely abstract problem.
On the one hand, what you're asking sounds like binary search. On the other hand, the adaptivity makes it feel more like active learning: think of the "thing you're trying to learn" as a hypothesis and the "queries" as being labels of objects. Since this question is primarily about finding the right terminology, do look at the active learning literature and see if it captures the spirit of what you want.