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• THE ADJACENCY MATRIX RECONSTRUCTION ALGORITHM FOR CAUSAL GRAPH MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES

  A. N. Tselykh , V.S. Vasilev, L. A. Tselykh

  2021-11-14

  Abstract ▼

  The paper deals with the problem of modeling complex systems in the absence of observable
  variables. To solve this problem, it is proposed to use causal graph models. The class of causal
  models considered here is defined as non-stochastic causal models with unobservable variables.
  These models are presented in the form of a directed graph, created on the basis of human mental
  representations. In this case, on the arcs, causality is expressed in the form of some marks with a
  sign that determines the direction of change in the state of the system. The considered causal models
  include heterogeneous, complex and qualitative types of variables that illustrate the nonnumerical
  nature of nodes and links and, as a consequence, the absence and impossibility of obtaining
  time series data. In the absence of observable variables and the impossibility of conducting
  experiments, the problem of reconstructing the adjacency matrix of the causal graph model becomes
  much more complicated. It is required to obtain a model with a certain spectral decomposition
  that implements the main function of the modeled system. Based on this concept, a new method
  for reconstructing the adjacency matrix is proposed, implemented on the basis of the corresponding
  causal propagation matrix or transmission matrix. The idea is to use combinatorial optimization
  based on spectral graph theory to generate data from a qualitative non-stochastic causal
  model and reconstruct an adjacency matrix using that data. In this case, the eigenvectors are
  identified as key objectives of the matrix reconstruction process, which postulates a fundamental
  approach based on the spectral properties of the graph. The results of computational experiments
  on solving the problem of reconstructing the adjacency matrix for causal graph models in the absence
  of observable variables using the developed algorithm have shown that the algorithm effectively
  reconstructs matrices from the given parameters with admissible similarity indices. The
  convergence of the approximation to the solution of the matrix reconstruction algorithm is proved
  no slower than with the speed of a geometric progression. From a technical point of view, the
  advantage of the algorithm is the implementation of a tool for automatic adjustment of the regularization
  parameter, suitable for users without prior mathematical knowledge.

• BIOINSPIRED ALGORITHM FOR SOLVING INVARIANT GRAPH PROBLEMS

  О.B. Lebedev, А.А. Zhiglatiy

  2022-11-01

  Abstract ▼

  A bioinspired method for solving a set of invariant combinatorial-logical problems on
  graphs is proposed: the formation of a graph matching, the selection of an internally stable set of
  vertices, and the selection of a graph clique. A modified paradigm of the ant colony is described,
  which uses, in contrast to the canonical method, the mechanisms for generating solutions on the
  search space model in the form of a star graph. The problem of forming an internally stable set of
  vertices in a graph can be formulated as a partitioning problem. At the initial stage, the same
  (small) amount of pheromone ξ/m, where m=|E|, is deposited on all edges of the star graph H.
  The process of finding solutions is iterative. Each iteration l includes three stages. Agents have
  memory. At each step t, the memory of the agent ak contains the amount of pheromone фj(t) deposited
  on each edge of the graph H. At the first stage, each agent ak of the population uses a constructive
  algorithm to find the solution Ur 0k, calculates the estimate of the solution ξk(Ur
  0k) and the value of the degree of suitability of the solution obtained by the agent φk (the amount of pheromone corresponding to the estimate). At the second stage, after the complete formation of solutions
  by all agents at the current iteration, the pheromone ωj accumulated in the j-th cell in the
  CEPб buffer array is added to each j-th cell of the main array Q2={qj|j=1,2,…,m} of the CEP0
  collective evolutionary memory. At the third stage, the general evaporation of the pheromone occurs
  on the set of edges E of the star graph H. The time complexity of the algorithm, obtained experimentally,
  coincides with theoretical studies and for the considered test problems is O(n2).

• INVESTIGATION OF APPLICABILITY OF MULTIMODEL DATA WAREHOUSES IN GAMING INDUSTRY

  А.А. Koblov , О.М. Romakina , А.S. Klemesheva , А. Z. Arseneva

  105-121

  2025-12-30

  Abstract ▼

  This paper examines the feasibility and effectiveness of using multi-model databases for storing and processing data in the gaming industry. Modern gaming projects are characterized by highly complex and heterogeneous data: from strictly structured information about players, items, and quests to semi-structured and tightly coupled data, such as recipe systems, dialog trees, clan relationships, and in-game encyclopedias. Existing approaches based on relational or single-model NoSQL storage systems often fail to provide the necessary flexibility, performance, and development ease for such complex scenarios. The aim of this study is to design and comparatively analyze the performance of a multi-model solution for typical gaming mechanics. The authors developed a multi-model storage structure based on the ArangoDB DBMS that integrates document, graph, and key-value data models. The solution architecture encompasses key RPG game components: player and inventory management, quest systems, dialogue, crafting recipes, loot tables, clan relationships, and full-text search of the in-game encyclopedia using ArangoSearch. The experimental section includes a detailed performance comparison of the developed multi-model storage system with the PostgreSQL relational DBMS and the MongoDB document DBMS on realistic datasets and queries. The results demonstrate a significant advantage of the multi-model approach when performing operations that require traversing complex relationships: for example, searching for hostile players through a clan relationship graph in ArangoDB is, on average, 11 times faster than a similar JOIN query in PostgreSQL. However, for scenarios with frequent modifications to linearly organized data (e.g., updating quest status), the multi-model storage system exhibits slightly lower performance compared to the relational model, which, however, is acceptable within the context of the overall game project architecture. The study confirms that multi-model DBMSs, particularly ArangoDB, represent a promising solution for the gaming industry, enabling efficient combination of different data models within a single platform, simplifying development, and achieving high performance on complex data, which is critical for modern multiplayer games.

• ALGORITHM OF EFFECTIVE CONTROLS FOR NONSTOCHASTIC CAUSAL MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES FOR SYSTEMS OF DECISION MAKING CONTROL

  A.N. Tselykh, V.S. Vasilev , L.A. Tselykh

  2021-11-14

  Abstract ▼

  The paper deals with the problem of reproducing the decision-making process by a person under
  conditions of uncertainty and incompleteness of the initial data. The decision-maker relies on his
  belief system, which includes a shared vision of the system in relation to which the decision is being
  made. The system is presented in the form of a causal model created on the basis of human mental
  representations. These models are directed graphs, on the arcs of which the causal relationship is
  expressed in the form of labels with a sign that determines the direction of change in the state of the
  system. The vertices of this directed graph are high-level abstraction concepts. This graph simulates
  the functioning of a real system. Thus, we investigate the problem of predicting and controlling human
  actions based on non-stochastic causal models in the absence of observable variables for use in
  decision support systems and expert systems. Decision-making is considered from the point of view of
  the choice of objects of application of managerial influences - the factors of the model. In this study,
  we show that the application of the proposed algorithm can facilitate decision-making regarding the
  choice of control actions that support the achievement of the tactical and strategic goals of the decision
  maker. It should be noted that the algorithm implements an automatic selection of the regularization
  parameter, which makes the development and application of the proposed algorithm available
  to users who do not have sufficient mathematical training. The convergence of the sequence of Lagrange
  multipliers of an effective control algorithm is proved. The theorem on resonance in a nonstochastic
  causal mod-el, represented by a directed graph, which is determined by the range of admissible
  values of the damping coefficient in the control model, is proved. It is expected that the introduction
  of this tool into decision support systems will in-crease the reliability of decisions regarding
  the operation of the system as a whole. The choice of control actions using the proposed algorithm
  has high efficiency and productivity. Thus, the results presented in the study can be useful for
  developing applications in intelligent systems.