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Classification of the dangerous situations in the decision support systems to control the technical objects

Keywords:

A.V. Savchenko - Ph.D. (Eng.), Associate Professor, National Research University Higher School of Economics; Doctoral-Candidate, Nizhniy Novgorod State Technical University n.a. R.E. Alekseev. E-mail: avsavchenko@hse.ru V.R. Milov - Dr.Sc. (Eng.), Professor, Head of Department, Nizhniy Novgorod State Technical University n.a. R.E. Alekseev. E-mail: vladimir.milov@gmail.com A.A. Sevryukov - Assistant, Nizhniy Novgorod State Technical University n.a. R.E. Alekseev. E-mail: ansev@mail.ru D.V. Milov - Post-Graduate Student, Nizhniy Novgorod State Technical University n.a. R.E. Alekseev. E-mail: milovdv@mail.ru


In this paper we focus on the problem of decision support in the elimination of dangerous situations discovered with the monitoring of the technical object\'s state. The situations classification task is reduced to the contextual multi-armed bandit problem. To solve this task, the principle of maximum expected utility is used. The output of the discussed decision support system is the list of ordered potential actions, among which the decision maker can choose the most appropriate one. An expected reward of each potential action is predicted by using the nonparametric Nadaraya-Watson kernel regression. We discuss the random strategies of the action choice, such as the estimation of the regression confidence interval (upper confidence bound method) and the simulated annealing with the Boltzmann exploration rule, in which the law of the temperature decrease depends on the specific domain. The experimental results are presented for the simple Bayesian network of the features generation. The discussed methods are compared with the ideal Bayesian classifier, in which the distribution of the feature vector for each state is assumed to be known. We experimentally showed, that the highest accuracy and its minimal standard deviation is achieved for the Boltzmann exploration. The superiority of the Boltzmann rule is especially noticeable for the small training samples. Finally, we discussed the potential enhancements of the descibed decision-making scheme, e.g. the investigation of the special case of the binary reward, processing of the state reflected in the complex features (photos/videos of the observed technical object, etc.).
References:

 

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