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Entropy
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Complex Systems Modeling and Analysis
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Complex Systems Modeling and Analysis

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (29 April 2022) | Viewed by 16371

Special Issue Editor

Dr. Claudia Szabo

E-Mail Website
Guest Editor
School of Computer Science, The University of Adelaide, Adelaide, SA 5005, Australia
Interests: complex systems; cloud computing; model driven engineering

Special Issue Information

Dear Colleagues,

Understanding the malign and benign properties of complex systems is critical as complexity becomes ubiquitous with the increase in system scale and the evolution of technology. Simulation, modeling and analysis techniques are fundamental in order to increase trust in the behavior of complex systems, in particular as they operate in dynamic environments or under unforeseen constraints. In addition, self-* such as self-organisation, self-healing, self-adaptation and self-optimization are becoming de-facto as a large number of systems employ several machine learning techniques in their quest to becoming autonomous. In this context, a good understanding of the complex system’s emergent behaviors, together with formal or informal guarantees about the system’s execution in particular settings will significantly advance the use of complex systems approaches in everyday life.

This Special Issue on “Complex Systems Modeling and Analysis” presents a platform where academic and industry researchers can present methodologies, techniques, applications and experiments that aim to increase our understanding of complex systems and their emergent behaviors. The focus of this Special Issue is both on modelling and simulation techniques but also on their practical application on various scenarios, and as such papers are welcome on a variety of topics including modelling, simulation, analysis, experimentation, and specific properties as defined above.

Dr. Claudia Szabo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • complex systems
  • emergent behavior
  • modeling and analysis
  • self-organisation
  • adaptability
  • self-*
  • autonomous systems

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Published Papers (4 papers)

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Research

18 pages, 2627 KiB  
Article
Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning
by Daming Shi, Xudong Guo, Yi Liu and Wenhui Fan
Entropy 2022, 24(6), 774; https://doi.org/10.3390/e24060774 - 30 May 2022
Cited by 3 | Viewed by 3461
Abstract
Poker has been considered a challenging problem in both artificial intelligence and game theory because poker is characterized by imperfect information and uncertainty, which are similar to many realistic problems like auctioning, pricing, cyber security, and operations. However, it is not clear that [...] Read more.
Poker has been considered a challenging problem in both artificial intelligence and game theory because poker is characterized by imperfect information and uncertainty, which are similar to many realistic problems like auctioning, pricing, cyber security, and operations. However, it is not clear that playing an equilibrium policy in multi-player games would be wise so far, and it is infeasible to theoretically validate whether a policy is optimal. Therefore, designing an effective optimal policy learning method has more realistic significance. This paper proposes an optimal policy learning method for multi-player poker games based on Actor-Critic reinforcement learning. Firstly, this paper builds the Actor network to make decisions with imperfect information and the Critic network to evaluate policies with perfect information. Secondly, this paper proposes a novel multi-player poker policy update method: asynchronous policy update algorithm (APU) and dual-network asynchronous policy update algorithm (Dual-APU) for multi-player multi-policy scenarios and multi-player sharing-policy scenarios, respectively. Finally, this paper takes the most popular six-player Texas hold ’em poker to validate the performance of the proposed optimal policy learning method. The experiments demonstrate the policies learned by the proposed methods perform well and gain steadily compared with the existing approaches. In sum, the policy learning methods of imperfect information games based on Actor-Critic reinforcement learning perform well on poker and can be transferred to other imperfect information games. Such training with perfect information and testing with imperfect information models show an effective and explainable approach to learning an approximately optimal policy. Full article
(This article belongs to the Special Issue Complex Systems Modeling and Analysis)
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15 pages, 4729 KiB  
Article
Knowledge Reuse of Multi-Agent Reinforcement Learning in Cooperative Tasks
by Daming Shi, Junbo Tong, Yi Liu and Wenhui Fan
Entropy 2022, 24(4), 470; https://doi.org/10.3390/e24040470 - 28 Mar 2022
Cited by 2 | Viewed by 2900
Abstract
With the development and appliance of multi-agent systems, multi-agent cooperation is becoming an important problem in artificial intelligence. Multi-agent reinforcement learning (MARL) is one of the most effective methods for solving multi-agent cooperative tasks. However, the huge sample complexity of traditional reinforcement learning [...] Read more.
With the development and appliance of multi-agent systems, multi-agent cooperation is becoming an important problem in artificial intelligence. Multi-agent reinforcement learning (MARL) is one of the most effective methods for solving multi-agent cooperative tasks. However, the huge sample complexity of traditional reinforcement learning methods results in two kinds of training waste in MARL for cooperative tasks: all homogeneous agents are trained independently and repetitively, and multi-agent systems need training from scratch when adding a new teammate. To tackle these two problems, we propose the knowledge reuse methods of MARL. On the one hand, this paper proposes sharing experience and policy within agents to mitigate training waste. On the other hand, this paper proposes reusing the policies learned by original teams to avoid knowledge waste when adding a new agent. Experimentally, the Pursuit task demonstrates how sharing experience and policy can accelerate the training speed and enhance the performance simultaneously. Additionally, transferring the learned policies from the N-agent enables the (N+1)–agent team to immediately perform cooperative tasks successfully, and only a minor training resource can allow the multi-agents to reach optimal performance identical to that from scratch. Full article
(This article belongs to the Special Issue Complex Systems Modeling and Analysis)
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9 pages, 2575 KiB  
Communication
Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode
by Jiancheng Sun, Zhinan Wu, Si Chen, Huimin Niu and Zongqing Tu
Entropy 2021, 23(8), 1071; https://doi.org/10.3390/e23081071 - 18 Aug 2021
Viewed by 2242
Abstract
Time series analysis has been an important branch of information processing, and the conversion of time series into complex networks provides a new means to understand and analyze time series. In this work, using Variational Auto-Encode (VAE), we explored the construction of latent [...] Read more.
Time series analysis has been an important branch of information processing, and the conversion of time series into complex networks provides a new means to understand and analyze time series. In this work, using Variational Auto-Encode (VAE), we explored the construction of latent networks for univariate time series. We first trained the VAE to obtain the space of latent probability distributions of the time series and then decomposed the multivariate Gaussian distribution into multiple univariate Gaussian distributions. By measuring the distance between univariate Gaussian distributions on a statistical manifold, the latent network construction was finally achieved. The experimental results show that the latent network can effectively retain the original information of the time series and provide a new data structure for the downstream tasks. Full article
(This article belongs to the Special Issue Complex Systems Modeling and Analysis)
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18 pages, 1159 KiB  
Article
Dynamic Modeling and Chaos Control of Informatization Development in Manufacturing Enterprises
by Peng Niu, Jianhua Zhu and Yanming Sun
Entropy 2021, 23(6), 681; https://doi.org/10.3390/e23060681 - 28 May 2021
Cited by 4 | Viewed by 2603
Abstract
To explore the cooperative evolutionary mechanism among top management support, employees’ technical ability, and informatization performance in the process of the “integration of informatization and industrialization (IOII)” in manufacturing enterprises, this study established a three-dimensional dynamic model of informatization development, obtained the model [...] Read more.
To explore the cooperative evolutionary mechanism among top management support, employees’ technical ability, and informatization performance in the process of the “integration of informatization and industrialization (IOII)” in manufacturing enterprises, this study established a three-dimensional dynamic model of informatization development, obtained the model parameters by the expert scoring method of case companies, and analyzed the time series of the dynamic model. After adjusting those parameters of the evolutionary process that do not meet the expectations of the enterprise, combined with management practice, the dynamic system is finally stable at the expected value. For a special state in the evolutionary process, the maximum Lyapunov exponent is used to identify the chaotic characteristics of the system, and a linear controller is designed to manage and control the chaotic system so that it evolves toward the expected value. The results of the case analysis verify the rationality of the model and the effectiveness of the control method, reveal the internal evolutionary mechanism of the informatization development of manufacturing enterprises, and explain the influence of chaos on enterprise management so as to help managers to use and control chaos. Full article
(This article belongs to the Special Issue Complex Systems Modeling and Analysis)
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