Price: 0
Number of applications: 0
22.12.25 (inclusive)
Contractual
Idea
ICT tasks
Robotics
Intelligent control systems
Software/ IS
Modern digital twins provide visualization and partial simulation of the state of objects, but they do not have the built-in intellectual ability to predict system behavior and make decisions in conditions of uncertainty. As a result, most existing solutions are limited to monitoring and displaying events after the fact, without providing deep proactivity and autonomous interaction between models.
The introduction of the Cognitive Multi-Agent Predictive Network will create a qualitatively new level of autonomy for digital twins, allowing them not only to reflect the current state of the object, but also to form deeply proactive behavior. The system will predict the development of processes, identify hidden anomalies and calculate the consequences of changes even before they occur in reality. This will ensure the transition from reactive monitoring to predictive management of infrastructure and facilities in real time, significantly increasing the stability and reliability of complex systems. Thanks to cognitive models and a distributed multi-agent architecture, digital twins will be able to coordinate their conclusions and decisions among themselves. This will lead to the formation of a collective intelligence of the infrastructure, when many heterogeneous objects — buildings, technical installations, transport hubs — act in concert, optimizing the behavior of the entire system without centralized management. This approach will make it possible to achieve optimal operating modes even in dynamic and highly uncertain conditions. The net effect is to significantly reduce operational risks, increase operational efficiency, and accelerate technical decision-making. The system will become the basis for future autonomous industries and "smart" cities, where digital counterparts will not just be a visualization tool, but independent participants in the management and adaptation of infrastructure. This creates a strategic advantage in scalability, reliability, and the ability to anticipate critical situations before they occur.
Grossul Pavel Pavlovich
Purpose and description of task (project)
To create an intelligent multi-agent system capable of independently analyzing, predicting and optimizing the behavior of digital counterparts of complex objects and infrastructures. The system should combine cognitive models, neural network predictive algorithms and distributed decision-making mechanisms, forming a single layer of "smart management" on top of physical and virtual environments. The project aims to develop the Cognitive Multi-Agent Predictive Network, a distributed network of autonomous computing agents that interact with each other and with digital counterparts in a simulation environment to solve analysis, forecasting, and management tasks. Each agent in the system must have the following capabilities: Cognitive interpretation of an object's state The agent analyzes the data of the digital twin (geometry, telemetry, events, historical records) and generates a model of the current state. Neural network predictive modeling Based on AI operators, including dynamic models (Neural PDE, Graph Neural Networks), the agent predicts the development of the system ahead in time: loads, risks, bottlenecks, potential accidents. Distributed decision-making Agents coordinate their actions through relational graph structures, ensuring consistent behavior without a central control node. Adaptation and self-study The system corrects internal models based on discrepancies between simulation and real observations, improving the accuracy of the digital twin's behavior. The project provides for the creation of a unified cognitive infrastructure embedded in digital twins, which will allow: to predict events before their actual occurrence; optimize processes and loads in the infrastructure; detect anomalies and threats; coordinate the operation of multiple virtual and physical objects; to form autonomous management strategies. The resulting system represents a new level of digital twins — not only reflecting a physical object, but also having built-in intelligence capable of predicting, analyzing, and interacting.
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