# Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs
## 1 Introduction  
One crucial issue that needs to be solved via monitoring and control systems for complex objects with dynamic structures (particularly monitoring and control systems for telecommunication networks) is to reconfigure networks in a timely manner, based on the current situation, and the options for its subsequent development. The necessity for network reconfiguration can arise due to the failure of one or more elements of the network, an increase or decrease in user activity, and further influencing factors, whose compositions and degrees of influence, as a rule, cannot be described a priori. Network reconfiguration requires the ability to predict the state of networks based on the data they provide. The task of predicting the state of complex objects is solved within both symbolic and sub-symbolic approaches [1, 2]. Despite significant advances in the area of neural networks developed within the sub-symbolic approach, their applications to network state prediction remain restricted, due to the complexity of the transition from single pulses, at the level of which the neural network (NN) operates, to the level of abstract symbols. Moreover, existing NN architectures do not provide deep (and, at the same time, fast) data processing. Intensive work aimed at the solution of the listed issues is being conducted, but the created architectures require more profound elaboration for further industrial application. Solving the task of prediction using symbolic methods requires a priori data about objects, their states, and behavior; moreover, most methods have high computational complexity, which leads to delays in data processing. The processing time is usually reduced by lowering theprecision and accuracy of the results being generated, which can lead to errors in making decisions about reconfiguration of networks. Given the current circumstances, a new approach to the multilevel inductive-deductive synthesis of object models with dynamic structure, suggested in [3], is of interest. The authors of [4] implemented a multilevel approach to solving the tasks of data processing and analyzing, as applied to the tasks of synthesis of the real object models, resulting in a multiple reduction of synthesis complexity. This makes it possible to use methods with high computational complexity, in practice. The objects considered in the article are multilevel with dynamic structures. Horizontal and vertical links established between the elements reflect constant and varying, in time, linear and non-linear dependences, which characterize the objects being observed and reflect their structures and behavior changes in time and space. Inductive synthesis methods allow reconstructing the object models from statistical and operational data coming from the objects; whereas deductive synthesis methods are used to prove that it is possible for the observed object to transfer to a given state in the future. Data on the expected state of the objects, as well as the transferring procedures between the different states, can be provided by the end users; furthermore, it can be received through processing statistical data obtained from observing the objects. The research is targeted to solve a class of problems on predicting the state of complex objects with variable structures that change, in time, under the influence of an aggregation of factors. Assumptions about the expected states of the objects at the end of the prediction period are not required. The problem is stated and solved in the following conditions: the objects have hierarchical structure; statistical data about the objects and the context in which they are operating can be obtained. To solve the class of problems stated above, the paper formulates the adaptive multilevel deductive synthesis within the development of the approach, which allows takes into account the context when predicting the states of the observed objects. The task is solved by using the mechanism of policies that control the model synthesis processes basing on the data coming from the objects.