Mobots (mobile robots) remote-controlled by human operators are more and more widely used for watching and guarding. A mobot should not be controlled by an occasional person. A human being has to show specific ability to be a good mobot operator. This ability is individual for each person and can be discovered with the help of an experiment which is time-consuming and expensive. Then the problem arises whether there is a simple and cheap test of this ability or not and, which is more technical, how to find out such a test. The paper addresses this problem. The experimental data are compared with those obtained with the help of two quick and cheap tests and some correlations between them are pointed out.

A description which summarizes entire and usually big set of data is called its model. The problem investigated in the paper consists in verification of models of data coming from a simulation experiment of selecting candidates for operators of mobile robot (more strictly building reliable predictive model of the data). The models are validated using train-and-test method and verified with the help of the EM (expectation-maximization) algorithm which was originally designed for solving clustering problems with missing data. Actually, the selecting is a clustering problem because the candidates are assigned to 'chosen', 'accepted' or 'rejected' subgroups. For such a case the missing data is the category (the subgroup) for which a candidate should be assigned on the basis of his activity measured during the simulation experiment. The paper explains the procedure of model verification. It also shows experimental results and draws conclusions.

This paper presents a part of research concerning an application of virtual reality for training operators of mobile robots (mobots). Mobots are often used for exploring areas dangerous or hardly accessible. It is obvious that the operator should not be a common person. In the paper a procedure of verification of comprehensive tests such as IQ test for initial selection of candidates for mobot operators is given and evaluated. *

<p>The paper presents an original method of dynamic classication of objects from a new domain which lacks an expert knowledge. The method relies on analysis of attributes of objects being classied and their general quality Q, which is a combination of particular object's attributes. The method uses a test of normality as a basis for computing the reliability factor of the classication (rfc), which indicates whether the classication and the model of quality Q are reliable. There is no need to collect data about all objects before the classication starts and possibly the best objects ale selected dynamically (on-the-y) while data concerning consecutive objects are gathered. The method is implemented as a software tool called Articial Classication Adviser (ACA). Moreover, the paper presents a case study, where the best candidates for reghting mobile robot operators are selected.</p>