George Rzevski, Petr Skobelev, Alexey Zhilyaev, Oleg Lakhin, Igor Mayorov, Elena Simonova. Ontology-Driven Multi-Agent Engine for Real Time Adaptive Scheduling // Proceedings of the International Conference on Control, Artificial Intelligence, Robotics and Optimization (ICCAIRO 2018), Prague, Czech Republic, May 19-21, 2018. – IEEE Xplore, IEEE. – P. 14-22. https://ieeexplore.ieee.org/document/8698403. DOI: 10.1109/ICCAIRO.2018.00011
The growing demand for improving business efficiency requests the development of generic resource management systems applicable for solving a wide range of complex problems with minimum cost and time. However, the classical combinatorial or heuristic methods and tools do not provide adequate solutions for solving complex problems of resource management in real time. That is why we consider multi-agent technology as the core part of such solutions – which helps find the balance of many interests and adapt it in a flexible way to unpredictable events, such as a new order, an unavailable resource, etc. In this paper we introduce the use of ontology for scheduling, which provides the opportunity to create ontological model of the enterprise, develop generic multi-agent scheduler and customize matching requirements for each operation in business or technological processes, for example, for applications in manufacturing, project management, supply chains, etc. Semantic Wikipedia on the top of ontology editor will be discussed to support knowledge base of enterprise for resource management. The example of applications for supply chain of insurance company is presented.
Petr O. Skobelev & Oleg I. Lakhin. Towards the digital platform and smart services for managing space traffic // International Journal of Design & Nature and Ecodynamics. – WIT Press, vol. 13(2018), no. 2. – pp. 187-198. DOI: 10.2495/DNE-V13-N2-187-198.
The objective of the paper is to discuss the increasing complexity of modern space traffic in the near-Earth space and outline the new approach for solving the problem. The requirements and functionality of digital platform for traffic management are presented and examples of problem solving are given. The developed approach will create new opportunities for managing space traffic and resources of the mission control centers for a large number of spacecrafts. Possible approaches to description of spacecraft flights are given. Methods and tools for optimizing the use of ground control complexes to manage large-scale orbital groups have been discussed. Creation of the digital platform and eco-system of smart services for space traffic management will solve the most important problem of space traffic management to increase the effectiveness of the created satellites groups and to protect the spacecrafts from space waste and debris.
Igor Mayorov, Petr Skobelev. Multi-Agent Technology in Real-time Intelligent Resource Management Systems // Proceedings of the 4th International Conference on Intelligent Systems and Applications (INTELLI 2015), October 11 — 16, 2015, St. Julians, Malta. – IARIA, P. 49-55.
The article describes the main principles of intelligent real-time resource management systems based on the use of multi-agent technology. Features of the new generation of systems are demonstrated that implement the full cycle of autonomous resource management, from reaction to real-world to monitoring deviations between the plan and the fact on the basis of the developed multi-agent platform. The article also presents several applications of scheduling systems in various areas, including cargo flow management for the International Space Station, workshop management in machine-building enterprises, railway traffic and cargo transportation management. Adaptability of multi-agent systems to external disruptive events is demonstrated. Finally, the similarities between multi-agent systems and non-equilibrium thermodynamics of Ilya Prigogine are described.
P.Skobelev. Multi-Agent Systems for Real Time Adaptive Resource Management. In Industrial Agents: Emerging Applications of Software Agents in Industry. Paulo Leitão, Stamatis Karnouskos (Ed.). – Elsevier. – 2015. – pp. 207-230.
In this chapter, the approach for developing multi-agent solutions for solving real-tame scheduling problems will be presented, as well as examples of commercial applications that have been running in day-to-day operations for several years and have produced measurable and proven benefits.
Petr Skobelev, Igor Mayorov, Sergey Kozhevnikov, Alexander Tsarev, Elena Simonova. Measuring adaptability of «swarm intelligence» for resource scheduling and optimization in real time // Proceedings of the 7th International Conference on Agents and Artificial Intelligence (ICAART 2015), Lisbon, Portugal, 10-12 January, 2015, vol. 2. – SCITEPRESS. – P. 517-522.
In this paper modern methods of scheduling and resource optimization based on the holonic approach and principles of “Swarm Intelligence” are considered. The developed classes of holonic agents and method of adaptive real time scheduling where every agent is connected with individual satisfaction function by the set of criteria and bonus/penalty function are discussed. In this method the plan is considered as a un-stable equilibrium (consensus) of agents interests in dynamically self-organized network of demands and supply agents. The self-organization of plan demonstrates a “swarm intelligence” by spontaneous autocatalitical reactions and other not-linear behaviours. It is shown that multi-agent technology provides a generic framework for developing and researching various concepts of “Swarm Intelligence” for real time adaptive event-driving scheduling and optimization. The main result of research is the developed approach to evaluate the adaptability of “Swarm Intelligence” by measuring improve of value and transition time from one to another unstable state in case of disruptive events processing. Measuring adaptability helps to manage self-organized systems and provide better quality and efficiency of real time scheduling and optimization. This approach is under implementation in multi-agent platform for adaptive resource scheduling and optimization. The results of first experiments are presented and future steps of research are discussed.
Skobelev Р. Bio-Inspired Multi-Agent Technology for Industrial Applications. Multi-Agent Systems – Modeling, Control, Programming, Simulations and Applications. Faisal Alkhateeb, Eslam Al Maghayreh and Iyad Abu Doush (Ed.). InTech Publishers, Austria, 2011. – pp. 495-522. ISBN: 978-953-307-174-9.
The paper describes a multi-agent system which is capable of achieving its goals under conditions of uncertainty and which exhibits emergent intelligent behaviour such as adaptation, learning and co-evolution with their environment. The intelligence of the scheduler emerges from the horizontal and vertical interaction of its constituent agents balancing their individual and group interests.