  To perform a precise auscultation for the purposes of examination of
respiratory system normally requires the presence of an experienced doctor.
With most recent advances in machine learning and artificial intelligence,
automatic detection of pathological breath phenomena in sounds recorded with
stethoscope becomes a reality. But to perform a full auscultation in home
environment by layman is another matter, especially if the patient is a child.
In this paper we propose a unique application of Reinforcement Learning for
training an agent that interactively guides the end user throughout the
auscultation procedure. We show that \textit{intelligent} selection of
auscultation points by the agent reduces time of the examination fourfold
without significant decrease in diagnosis accuracy compared to exhaustive
auscultation.

Sound;Artificial Intelligence;Machine Learning;Audio and Speech Processing