Abstract. An automated search with human involvement consisting of two
stages is given detailed consideration in this paper. In the first stage, a search
without direct human involvement is implemented. In the second stage, the
search assumes human involvement. Evaluations of the search operations’
effectiveness are presented. These operations are implemented for searching one
object exclusively among a variety of similar objects. The average number of
similar objects recommended for further analysis was used as effectiveness
indicator. A set of numerical evaluation criteria for search effectiveness is
introduced. The basis of the search block is a pattern recognition algorithm
characterized by two probabilities: 1) probability of missing a target, and 2) false
alarm probability. An analytical model of the search block was developed. In this
paper particular attention is given to the average length of the recommendatory
list as an effectiveness indicator. Four properties of this indicator were
determined.