  Herein we suggest a mobile robot-training algorithm that is based on the
preference approximation of the decision taker who controls the robot, which in
its turn is managed by the Markov chain. Setup of the model parameters is made
on the basis of the data referring to the situations and decisions involving
the decision taker. The model that adapts to the decision taker's preferences
can be set up either a priori, during the process of the robot's normal
operation, or during specially planned testing sessions. Basing on the
simulation modelling data of the robot's operation process and on the decision
taker's robot control we have set up the model parameters thus illustrating
both working capacity of all algorithm components and adaptation effectiveness.
