Испоьзование методов искусственного интеллекта в изучении личности серийных убийц
Modern criminalists do not share a common opinion regarding the choice of parameters which could be used to work out a system of characteristics to differentiate a maniac killer from an ordinary person. This hinders the development of efficient software for investigation purposes. The paper describes the experience of developing a neural network that can learn using data about known serial killers, including their biological, social and psychological parameters. The authors also evaluate the errors of this neural network’s mathematical model, prove its adequacy and present a study which allowed to work out a comparative evaluation of the impact that different parameters have on the modeling result, i.e. a person’s predisposition to violence. They prove that the most important parameters include: a psychiatric disorder, alcoholic parents, growing up with parents, family status, social status. The impact that some parameters have on the predisposition to committing serial murders is shown using the data of actual persons in virtual computer experiments. Their results revealed some interesting regularities. For example, it was shown that if some maniac killers had children, this would reduce their predisposition to violence whereas for some others it would not. The same was observed when researching the impact of other parameters, such as the gravity of the psychiatric disorder, social and family status, etc. To determine how predisposed to serial crimes a certain person is, it is necessary to conduct a complex analysis of all the parameters in a mathematical model. Computer software based on this mathematical model was adapted for crime detection specialists. It is in open access on the website of Perm Branch of Artificial Intelligence Methodology Research Council of Russian Academy of Sciences. This software can be used by investigators of serial crimes at the initial stages of investigation when it is necessary to process large volumes of data regarding potential suspects.