Special Issue "Multi-Agent Systems 2019"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 April 2019

Special Issue Editors

Guest Editor
Prof. Dr. Andrea Omicini

Department of Computer Science and Engineering (DISI) Alma Mater Studiorum – Università di Bologna, 47521 Cesena, Italy
Website | E-Mail
Phone: +39-0547-338875
Interests: distributed systems; coordination; agents & multiagent systems; software engineering; intelligent systems; multi-paradigm programming languages; simulation; self-organisation
Guest Editor
Dr. Stefano Mariani

Department of Sciences and Methods for Engineering, Università degli Studi di Modena e Reggio Emilia, Modena 41121, Italy
Website | E-Mail
Interests: agent-based models and technologies; agent-oriented programming; multi-agent systems; coordination models & languages; coordination technologies; nature-inspired algorithms; self-organisation; knowledge management; IoT paradigms and infrastructure

Special Issue Information

Dear Colleagues,

Research work done on intelligent agents and multi-agent systems has matured during the past decade, and many effective applications of this technology are currently being deployed. Although computational approaches for multi-agent systems have mainly emerged in the past few decades, scholars have been prolific with the variety of methods proposed to solve this paradigm. Different communities have emerged with multi-agent systems as their main research topic.

Multi-agent systems allow the development of distributed and intelligent applications in complex and dynamic environments. Systems of this kind play a crucial role in life, evidenced by the broad range of applied areas involved in their use, including manufacturing, management sciences, e-commerce, biotechnology, etc.

There are many reasons for the interest of researchers in this new discipline. Firstly, computational systems have gradually shifted towards a distributed paradigm where heterogeneous entities with different goals can enter and leave the system dynamically and interact with each other. Secondly, new computational systems should be able to negotiate with one another, typically on behalf of humans, in order to come to mutually acceptable agreements. As a consequence, autonomy, interaction, mobility, and openness are key concepts studied in the area.

The purpose of this Special Issue is to make known some of the advances made in this paradigm and try to show the current state of this technology by analysing different aspects, as well as its possible application to various domains. This review of the current state-of-the-art is not intended to make an exhaustive exploration of all the current existing works, but rather to try to give an overview of the research in agent technology, showing the high level of activity of this area.

Prof. Dr. Andrea Omicini
Dr. Stefano Mariani
Guest Editors

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 papers will be 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1500 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

  • agent and multi-agent applications 
  • agent engineering: development techniques, tools, and platforms 
  • agent-based simulation: development techniques, tools, and platforms 
  • biologically-inspired approaches and methods 
  • agent-based collective intelligence 
  • multi-agent complex systems 
  • agent-based distributed problem solving 
  • human–robot/agent interaction 
  • agent-based intelligent control and manufacturing systems 
  • learning and adaptation in multi-agent systems 
  • methodologies for agent-based systems 
  • multi-robot systems 
  • negotiation and conflict resolution in multi-agent systems 
  • norms for multi-agent systems 
  • institutions for multi-agent systems 
  • reasoning in agent-based systems 
  • self-organization in multi-agent systems 
  • single and multi-agent planning and scheduling
  • agent-based socio-technical systems 
  • teamwork, team formation, teamwork analysis 
  • trust and reputation in multi-agent systems

Published Papers (2 papers)

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Research

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Open AccessArticle Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions
Appl. Sci. 2019, 9(5), 1004; https://doi.org/10.3390/app9051004
Received: 2 January 2019 / Revised: 27 February 2019 / Accepted: 6 March 2019 / Published: 11 March 2019
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Abstract
In recent years, the formation control of multi-mobile robots has been widely investigated by researchers. With increasing numbers of robots in the formation, distributed formation control has become the development trend of multi-mobile robot formation control, and the consensus problem is the most [...] Read more.
In recent years, the formation control of multi-mobile robots has been widely investigated by researchers. With increasing numbers of robots in the formation, distributed formation control has become the development trend of multi-mobile robot formation control, and the consensus problem is the most basic problem in the distributed multi-mobile robot control algorithm. Therefore, it is very important to analyze the consensus of multi-mobile robot systems. There are already mature and sophisticated strategies solving the consensus problem in ideal environments. However, in practical applications, uncertain factors like communication noise, communication delay and measurement errors will still lead to many problems in multi-robot formation control. In this paper, the consensus problem of second-order multi-robot systems with multiple time delays and noises is analyzed. The characteristic equation of the system is transformed into a quadratic polynomial of pure imaginary eigenvalues using the frequency domain analysis method, and then the critical stability state of the maximum time delay under noisy conditions is obtained. When all robot delays are less than the maximum time delay, the system can be stabilized and achieve consensus. Compared with the traditional Lyapunov method, this algorithm has lower conservativeness, and it is easier to extend the results to higher-order multi-robot systems. Finally, the results are verified by numerical simulation using MATLAB/Simulink. At the same time, a multi-mobile robot platform is built, and the proposed algorithm is applied to an actual multi-robot system. The experimental results show that the proposed algorithm is finally able to achieve the consensus of the second-order multi-robot system under delay and noise interference. Full article
(This article belongs to the Special Issue Multi-Agent Systems 2019)
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Open AccessProject Report A Multi-Agent Based Intelligent Training System for Unmanned Surface Vehicles
Appl. Sci. 2019, 9(6), 1089; https://doi.org/10.3390/app9061089
Received: 10 February 2019 / Revised: 6 March 2019 / Accepted: 6 March 2019 / Published: 15 March 2019
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Abstract
The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) [...] Read more.
The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs’ group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent-based data collection platform is deployed to gather competition data from agents’ observation for on-line learning tasks. Second, we design a genetic-based fuzzy rule training algorithm that is capable of optimizing agents’ coordination decisions in an accumulated manner. The simulation results of this study demonstrate that our proposed training approach is feasible and able to converge to a stable action selection policy towards efficient multi-USVs’ cooperative decision making. Full article
(This article belongs to the Special Issue Multi-Agent Systems 2019)
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