Valery Vilisov

Valery Vilisov

This work suggests the estimation method developed in relation to the position of the robotic system (RS) operator, showing his degree of risk proneness. The base models are: Hurwitz pessimism/optimism criterion and decision trees. The problem is solved using the reverse setting: we estimate pessimism/optimism parameter of the operator (decision taker) by observing what decisions he makes when controlling the RS. The solution context of such decision taker position estimation problems can be: using RS in emergency situations, in military actions and other situations connected with the uncertainty of the situation.

Valery Vilisov

Paper provides a method for solving the reverse Monge-Kantorovich transport problem (TP). It allows to accumulate positive decision-taking experience made by decision-taker in situations that can be presented in the form of TP. The initial data for the solution of the inverse TP is the information on orders, inventories and effective decisions take by decision-taker. The result of solving the inverse TP contains evaluations of the TPs payoff matrix elements. It can be used in new situations to select the solution corresponding to the preferences of the decision-taker. The method allows to gain decision-taker experience, so it can be used by others. The method allows to build the model of decision-taker preferences in a specific application area. The model can be updated regularly to ensure its relevance and adequacy to the decision-taker system of preferences. This model is adaptive to the current preferences of the decision taker.

Valery Vilisov

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.

Valery Vilisov

The object of the research is the adaptive algorithms that are used by the operator when educating the robotic systems. Operator, being the target-setting subject, is interested in the goal that robotic systems, being the conductor of his targets (criteria), would provide a maximum effectiveness of these targets' (criteria's) achievement. Thus, the adaptive algorithms provide the adequate reflection of the operator's goals, found in the robotic systems' actions. This work considers potential possibilities of such target adaption of the robotic systems used for the class of the allocation problems.

Valery Vilisov

The development of the works of the author about adaptive algorithms of teaching the robotic systems with the help of operator is described here. An operator is assumed to be an experience decision-maker and sane carrier of a target which the robotic system needs to achieve. The works characteristic is that the behavior of the robotic system is not specified a priori (as standard) but is formed adaptively based on the information about the situation and decisions made by a decision-maker. In this scheme the robotic system and the decision-maker can cooperate in the normal operation mode of the robotic system or in the time sharing mode with the possibility to plan actively the experiment on the robotic system. If the adaptive scheme is chosen, there are teaching stages and operating stages of the robotic system. At that the decision-maker can act slowly having the possibility to weigh the decision made. This way allows the robotic system reacting flexibly by switching between preset models and respond to the environment instability. The data integrity about the environment condition and about target preferences of an operator plays a very important role in robotic system work. The effective work of the robotic system depends on the effective settings of a preference model of the robotic system based on the decisions of the decision-maker and on the effective control. The influence of settings and control factors on the index of effectiveness of the robotic system is subject of this work. The uncertainty may be caused by the data flow limitation received by the operator on the stage of the model setting.