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Optimal control for Stochastic Multi-agent systems With the Use of Parallel Hybrid Genetic Algorithm 4th

Optimal Control for Stochastic Multi-agent Systems With the...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Akopov, Andranik S. Beklaryan, Armen L. Central Economics and Mathematics Institute of RAS Moscow Russia Moscow Institute of Physics and Technology Moscow Russia HSE University Moscow Russia

ISBN: (纸本)9783031812408

In modern times, stochastic large-scale multi-agent systems (MAS) aimed at supporting socio-economic planning are being developed. There is a well known problem of a high computational complexity task of an optimal control for multiple agents’ behaviour in models of random interactions. In particular, agents (such as sellers and buyers) should make individualised decisions on establishing interconnections to exchange products, money or information at each moment of time. Such decisions affect the values of agents’ utility functions that, as a rule, should be maximised. In fact, each agent forms the set of individual states that define whether or not the interaction with other agents is allowed at moments of time. As a result, the dynamic programming method should be applied at the individual level of each agent maximising its own utility function, that is the extremely complex task. To overcome appropriate difficulties and seek suboptimal individual decisions in such MAS, a novel parallel hybrid real-coded genetic algorithm has been developed. The proposed hybrid method combines the use of the real-coded genetic algorithm (RCGA) for an evolutionary search, particle swarm optimisation for reducing the necessary number of model recalculations and periodically engaging ANN-based surrogate models for the fitness-function approximation. The approach allows researchers to significantly improve the time-efficiency of seeking optimal individualised decisions in MAS while keeping up their quality. Moreover, the distribution type can be one of the decision variables used in RCGAs for maximising the utility function. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic systems

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Al-enabled Decision Aid for Solving complex problems in a Judge-advisor Setting 25

Al-enabled Decision Aid for Solving Complex problems in a Ju...

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2022 IEEE international conference on Automation/25th Congress of the Chilean Association of Automatic control, ICA-ACCA 2022

作者： Kacprzyk, Janusz Systems Research Institute Polish Academy of Sciences Ul. Newelska 6 Warsaw01-447 Poland

ISBN: (纸本)9781665494083

Decision making, the most common and frequent human activity, tends to proceed in more and more complex problem settings and environments, with many stkeholders, much uncertain, imprecise and missing information, a high changeability of characteristics and parameters, multiple criteria, goals or aspects, etc. Moreover, in virtually all non-trivial systems a crucial role is played by the human being in the decision process. In many, of not most cases of complex problems an effective and efficient approach is to assume the decision making to proceed in a setting in which there is a domain expert the so-called judge, who has a deep expertise in his domain, e.g., city transportation, but not in the solution tools, e.g. optimization, and an additional expert, the so-called advisor, who has a deep expertise in the solution methods but not necessarily in the domain. Then, the so-called judge-advisor paradigm can be applied as shown in the presentationm with an analysis of advice giving, taking, and rejecting. These processes can be amplified by a wide use of AT tools and technique leading to the co-called Al-enabled approaches. The above issues will be considered, mainly from the point of view of decision aid but with some remarks on decision supprt and recomendation. Some examples will be mentioned. © 2022 IEEE.

关键词： Decision making

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FuzzBtor2: A Random Generator of Word-Level Model Checking problems in Btor2 Format 29th

FuzzBtor2: A Random Generator of Word-Level Model Checking P...

引用

29th international conference on Tools and Algorithms for the Construction and Analysis of systems (TACAS)

作者： Xiao, Shengping Zhang, Chengyu Li, Jianwen Pu, Geguang East China Normal Univ Shanghai Peoples R China Swiss Fed Inst Technol Zurich Switzerland Shanghai Trusted Ind Control Platform Co Ltd Shanghai Peoples R China

ISBN: (纸本)9783031308192;9783031308208

We present FuzzBtor2, a fuzzer to generate random word-level model checking problems in Btor2 format. Btor2 is one of the mainstream input formats for word-level hardware model checking and was used in the most recent hardware model checking competition. Compared to bit-level one, word-level model checking is a more complex research field at an earlier stage of development. Therefore, it is necessary to develop a tool that can produce a large number of test cases in Btor2 format to test either existing or under-developed word-level model checkers. To evaluate the practicality of FuzzBtor2, we tested the state-of-the-art word-level model checkers AVR and Pono with the generated benchmarks. Experimental results show that both tools are buggy and not mature enough, which reflects the practical value of FuzzBtor2.

关键词： Model checking

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Fast-Growing Companies Development control

Fast-Growing Companies Development Control

引用

international conference on Soft Computing and Measurements (SCM)

作者： Oleg I. Dranko Aleksandr F. Rezchikov Anatolij D. Tsvirkun Vadim A. Kushnikov Aleksej S. Bogomolov Iraida A. Stepanovskaya Laboratory of Large-Scale Systems Development V. A. Trapeznikov Institute of Control Scienes of RAN Moscow Russia Institute of Precision Mechanics and Control Saratov Federal Scientific Center of the RAS Saratov Russia

The paper is devoted to the development of a modeling complex for managing the development of fast-growing companies based on technologies for integrating, processing and visualizing big data about the country’s ente... 详细信息

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

关键词： Process control Data visualization Companies Big Data Data science control systems Data models

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Research on the Equilibrium Strategy of UAV Attack and Defense Game Based on MCCFVFP

Research on the Equilibrium Strategy of UAV Attack and Defen...

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Advanced Algorithms and control Engineering (ICAACE), international conference on

作者： Falin Hei Qi Ju Yunfeng Luo Zhemei Fang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

The unmanned aerial vehicle (UAV) attack and defense (A&D) confrontation can be modeled as a game problem. However, existing research does not fully consider the incompleteness of game information, making it difficult to apply in complex and unknown UAV A&D scenarios. Game theory provides a tool for the modeling and analysis of UAV A&D. Firstly, this paper establishes an incomplete information dynamic game model for UAV A&D and abstracts the scenario to simplify the complex environment and UAV movement. Secondly, an improved Counterfactual Regret Minimization algorithm is introduced to solve the model. Through Monte Carlo sampling, the efficiency of solving complex game problems is improved. Finally, the equilibrium strategy obtained from the solution is verified by experimental simulation, effectively demonstrating the feasibility and effectiveness of the algorithm.

关键词： Analytical models Monte Carlo methods control engineering Heuristic algorithms Games Autonomous aerial vehicles Minimization complexity theory Vehicle dynamics Game theory

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Research on Great Lake Water Level Based on NSGA-II and MPC control Models

Research on Great Lake Water Level Based on NSGA-II and MPC ...

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Electrical, Automation and Computer Engineering (ICEACE), international conference on

作者： Zhiquan Liu Xiaoxiao Li Yuqin Chen Shanghai Lixin University of Accounting and Finance Shanghai China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

NSGA-II is an algorithm widely used to solve multi-conflict target optimization problems, and the MPC control model is an optimization strategy framework for multi-factor control. In this paper, we combine the two to explore their application in multi-objective and multi-factor scenarios and conduct an empirical analysis on the water level of The Great Lake. Firstly, a multi-objective optimization function was established through NSGA-II to maximize the overall interests of The Great Lake water level stakeholders. Subsequently, the MPC water level control system was used to construct a water level control model considering multiple influencing factors, which made it possible to maximize the interests of the owners and avoid the risk of natural disasters within the control of the water level. The research will not only help improve the well-being of stakeholders, but also provide strong support for the response to global climate change and the realization of green development.

关键词： Fluctuations Disasters Computational modeling Green products Optimization methods Lakes Stakeholders Level control Sustainable development complex systems

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Multi-agent deep reinforcement learning for traffic signal control with Nash Equilibrium 23

Multi-agent deep reinforcement learning for traffic signal c...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wei, Wei Wu, Qiang Wu, Jianqing Du, Bo Shen, Jun Li, Tinghong School of Computer Science and Engineering Xi'an University of Technology Xi'an China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China University of Wollongong Smart Infrastructure Facility Wollongong Australia China Mobile Communications Corporation Lanzhou China

ISBN: (纸本)9781665494571

Traffic signal control is an essential and chal-lenging real-world problem, which aims to alleviate traffic congestion by coordinating vehicles' movements at road in-tersections. Deep reinforcement learning (DRL) combines deep neural networks (DNNs) with a framework of reinforcement learning, which is a promising method for adaptive traffic signal control in complex urban traffic networks. Now, multi-agent deep reinforcement learning (MARL) has the potential to deal with traffic signal control at a large scale. However, current traffic signal control systems still rely heavily on simplified rule- based methods in practice. In this paper, we propose: (1) a MARL algorithm based on Nash Equilibrium and DRL, namely Nash Asynchronous Advantage Actor-Critic (Nash-A3C);(2) an urban simulation environment (SENV) to be essentially close to the real-world scenarios. We apply our method in SENV, obtaining better performance than benchmark traffic signal control methods by 22.1%, which proves that Nash-A3C to be more suitable for large industrial level deployment. © 2021 IEEE.

关键词： Performance evaluation Deep learning Computational modeling Roads Neural networks Reinforcement learning Nash equilibrium

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modeling Errors and Failures in Automated Human-Machine control systems to Solve Reliability Assessment Tasks 8

Modeling Errors and Failures in Automated Human-Machine Cont...

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8th IEEE international conference on problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Lavrov, Evgeniy Siryk, Olga Sedova, Nelly Paderno, Pavel Burkov, Evgeniy Vakal, Svetlana Sumy State University Sumy Ukraine Taras Shevchenko National University of Kyiv Kyiv Ukraine Maritime State University Named After G.I. Nevelskoy Vladivostok Russia Saint Petersburg State Electrotechnical University St. Petersburg Russia

ISBN: (纸本)9781665406826

It is shown that the role of human factor in modern man-machine systems is increasing and the need to search for ergonomic reserves to improve efficiency is substantiated. The apparatus of functional networks by Professors Anatoly Gubinsky, Vladimir Evgrafov and AkivaAsherov is chosen as a base for modeling man-machine interaction. It is shown how to provide analytical estimation of reliability by transition from 'graph of actions' to 'graph of appearance and elimination of errors'. A fragment of a library of computational models for typical functional structures is given. In addition to analytical modeling, the technology of simulation modeling in MATLAB-Simulink-Stateflow environment is proposed. The development will be useful for inclusion in control systems of complex man-machine interaction in telecommunications, manufacturing, and e-learning. © 2021 IEEE.

关键词： Ergonomics

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Computer Simulation of Dynamic Processes in Electronic systems

Computer Simulation of Dynamic Processes in Electronic Syste...

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2022 IEEE international Multi-conference on Engineering, Computer and Information Sciences, SIBIRCON 2022

作者： Timofeev, Konstantin A. Shornikov, Yury V. Novosibirsk State Technical University Automated Control Systems Department Novosibirsk Russia Automated Control Systems Department Institute of Automation and Electrometry The Siberian Branch of the Russian Academy of Sciences Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9781665464802

The numerical simulation of electronic devices is associated with the solution of stiff differential-algebraic equations systems. An important factor in effective numerical simulation is the count of calculations of the Jacobi matrix, as well as ensuring the necessary accuracy of calculations without the need for a significant reduction in the integration *** article presents a model of a ring modulator in the SimInTech software package. A comparative analysis of the effectiveness of explicit adaptive methods and Diagonally Implicit Runge-Kutta methods implemented in the software systems SimInTech and LISMA has been carried *** is concluded that the DIRK4 method in SimInTech is effective on the example of essentially stiff problems that arise when modeling electronic devices. Compared to the Gear method (in the SimInTech version), the DIRK4 method has almost 3 times fewer decompositions of the Jacobi matrix, which has a good effect on performance with an increase in the system dimension. © 2022 IEEE.

关键词： Integration

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Ungar - A C++ Framework for Real-Time Optimal control Using Template Metaprogramming

Ungar - A C++ Framework for Real-Time Optimal Control Using ...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Flavio De Vincenti Stelian Coros Computational Robotics Lab ETH Zurich Switzerland

We present Ungar, an open-source library to aid the implementation of high-dimensional optimal control problems (OCPs). We adopt modern template metaprogramming techniques to enable the compile-time modeling of complex systems while retaining maximum runtime efficiency. Our framework provides syntactic sugar to allow for expressive formulations of a rich set of structured dynamical systems. While the core modules depend only on the header-only Eigen and *** libraries, we bundle our codebase with optional packages and custom wrappers for automatic differentiation, code generation, and nonlinear programming. Finally, we demonstrate the versatility of Ungar in various model predictive control applications, namely, four-legged locomotion and collaborative loco-manipulation with multiple one-armed quadruped robots. Ungar is available under the Apache License 2.0 at https://***/fdevinc/ungar.

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