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.
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