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Applied Sciences
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Advances in Multi-Agent Systems
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Advances in Multi-Agent Systems

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

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 20232

Special Issue Editors

Prof. Dr. Andrea Omicini

E-Mail Website
Guest Editor
Department of Computer Science and Engineering (DISI) Alma Mater Studiorum – Università di Bologna, 47521 Cesena, Italy
Interests: distributed and pervasive systems; agents and multiagent systems; software engineering; intelligent systems; multi-paradigm programming languages; simulation; self-organization
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Mariani

E-Mail Website
Guest Editor
Department of Sciences and Methods for Engineering, Università degli Studi di Modena e Reggio Emilia, 41121 Modena, Italy
Interests: coordination; socio-technical systems; Internet of Things; self-organization; multi-agent systems; computational argumentation; AI in healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research work on intelligent agents and multi-agent systems (MAS) has steadily matured during the last decades. Many effective applications of the resulting technologies have actually been deployed, which has enabled the development of distributed and intelligent applications in complex and highly-dynamic environments. Systems of this sort play a crucial role in people’s everyday life, as evidenced by the broad range of applications relying on agent-based solutions, including manufacturing, management sciences, e-commerce, biotechnology, healthcare, etc.

The field of MAS is a strongly inter-disciplinary research area of interest to highly heterogeneous communities. This is witnessed by the many events and publications fostering the application of MAS to specific business domains, and the convergence of research in logics, automated learning, planning, software engineering, and other disciplines contributing to the very notion of agent technology.

There are many reasons for researchers to be interested in this 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, 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 in the area.

The purpose of this Special Issue is to advance the MAS field, making it more visible and accessible to the scientific community. It will show the current state of the resulting technologies by analyzing all the relevant scientific and technical aspects as well as their possible application to various domains. This review of the current state-of-the-art is not intended to be an exhaustive exploration of all existing works, but rather to give an overview of the current research in agent and MAS technology, and 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 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. 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 2400 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-based engineering—development of techniques, tools, and platforms;
  • agent-based simulation—development of techniques, tools, and platforms;
  • biologically-inspired approaches and methods for multi-agent systems;
  • agent-based collective intelligence;
  • agent-based distributed problem solving;
  • human–robot/agent interaction;
  • 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;
  • multi-agent planning;
  • agent-based socio-technical systems;
  • trust and reputation in multi-agent systems;
  • agents applied to cyber–physical systems (as the Internet of Things);
  • convergence of blockchain (and smart contract) and multi-agent systems technology;
  • convergence of agents and machine learning techniques

Published Papers (6 papers)

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Editorial

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4 pages, 1113 KiB  
Editorial
Special Issue “Advances in Multi-Agent Systems”: Editorial
by Stefano Mariani and Andrea Omicini
Appl. Sci. 2023, 13(5), 3027; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053027 - 27 Feb 2023
Cited by 1 | Viewed by 1085
Abstract
Multi-agent systems (MAS) are collections of autonomous computational entities (the agents) capable of pro-actively pursuing goals and re-actively adapting to environment change. Agents in MAS exploit their social abilities, interacting with peers, and their situated capabilities as well, by perceiving and acting on [...] Read more.
Multi-agent systems (MAS) are collections of autonomous computational entities (the agents) capable of pro-actively pursuing goals and re-actively adapting to environment change. Agents in MAS exploit their social abilities, interacting with peers, and their situated capabilities as well, by perceiving and acting on the world around them. From distributed computing to intelligent systems, the relevance of agents and MAS as software abstractions is steadily growing as they are extensively and increasingly used to model, simulate, and build heterogeneous systems across a huge variety of diverse application scenarios and business domains, ranging from industrial manufacturing to robotics, from social simulation to applications, and more. The recent, renewed popularity of AI techniques has further spread the adoption of MAS, focusing in particular on the cognitive capabilities of agents, so that intelligent systems can be modelled and built as MAS. Along those lines, this Special Issue gathers five contributions that well represent the many diverse advancements that are currently ongoing in the MAS field. Full article
(This article belongs to the Special Issue Advances in Multi-Agent Systems)
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Research

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14 pages, 557 KiB  
Article
Adaptation to Other Agent’s Behavior Using Meta-Strategy Learning by Collision Avoidance Simulation
by Kensuke Miyamoto, Norifumi Watanabe and Yoshiyasu Takefuji
Appl. Sci. 2021, 11(4), 1786; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041786 - 18 Feb 2021
Cited by 5 | Viewed by 1365
Abstract
In human’s cooperative behavior, there are two strategies: a passive behavioral strategy based on others’ behaviors and an active behavioral strategy based on the objective-first. However, it is not clear how to acquire a meta-strategy to switch those strategies. The purpose of the [...] Read more.
In human’s cooperative behavior, there are two strategies: a passive behavioral strategy based on others’ behaviors and an active behavioral strategy based on the objective-first. However, it is not clear how to acquire a meta-strategy to switch those strategies. The purpose of the proposed study is to create agents with the meta-strategy and to enable complex behavioral choices with a high degree of coordination. In this study, we have experimented by using multi-agent collision avoidance simulations as an example of cooperative tasks. In the experiments, we have used reinforcement learning to obtain an active strategy and a passive strategy by rewarding the interaction with agents facing each other. Furthermore, we have examined and verified the meta-strategy in situations with opponent’s strategy switched. Full article
(This article belongs to the Special Issue Advances in Multi-Agent Systems)
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20 pages, 5170 KiB  
Article
Multi-Agent Simulation Environment for Logistics Warehouse Design Based on Self-Contained Agents
by Takumi Kato and Ryota Kamoshida
Appl. Sci. 2020, 10(21), 7552; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217552 - 27 Oct 2020
Cited by 6 | Viewed by 3748
Abstract
We propose a multi-agent simulation environment for logistics warehouses. Simulation is a crucial part of designing industrial systems, such as logistics warehouses. A warehouse is a multi-agent system (MAS) that consists of various autonomous subsystems with robots, material-handling equipment, and human workers. It [...] Read more.
We propose a multi-agent simulation environment for logistics warehouses. Simulation is a crucial part of designing industrial systems, such as logistics warehouses. A warehouse is a multi-agent system (MAS) that consists of various autonomous subsystems with robots, material-handling equipment, and human workers. It is generally difficult to analyze the performance of a MAS thus, it is important to model a warehouse and conduct simulations to design and evaluate the possible system configurations. However, the cost of modeling warehouses and modifying the models is high because there are various components and interactions compared to conventional multi-agent simulations. We proposed a self-contained agent architecture and message architecture of a multi-agent simulation environment for logistics warehouses to reduce the simulation-model development and modification costs. We quantitatively evaluated our environment in terms of development costs by comparing such costs of our environment and a widely used multi-agent simulation environment. Full article
(This article belongs to the Special Issue Advances in Multi-Agent Systems)
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20 pages, 6571 KiB  
Article
An Event-Driven Agent-Based Simulation Model for Industrial Processes
by Vincenzo Iannino, Claudio Mocci, Marco Vannocci, Valentina Colla, Andrea Caputo and Francesco Ferraris
Appl. Sci. 2020, 10(12), 4343; https://0-doi-org.brum.beds.ac.uk/10.3390/app10124343 - 24 Jun 2020
Cited by 12 | Viewed by 4283
Abstract
Process manufacturing industries are complex and dynamic systems composed of several processes, subject to many operations and unexpected events that can compromise overall system performance. Therefore, the use of technologies and methods that can transform traditional process industries into smart factories is necessary. [...] Read more.
Process manufacturing industries are complex and dynamic systems composed of several processes, subject to many operations and unexpected events that can compromise overall system performance. Therefore, the use of technologies and methods that can transform traditional process industries into smart factories is necessary. In this paper, a smart industrial process based on intelligent software agents is presented with the aim of providing a technological solution to the specific needs of the process industry. An event-driven agent-based simulation model composed of eight reactive agents was designed to simulate and control the operations of a generic industrial process. The agents were modeled using the actor approach and the communication mechanism was based on the publish–subscribe paradigm. The overall system was tested in different scenarios, such as faults, changing operating conditions and off-spec productions. The proposed agent-based simulation model proved to be very efficient in promptly reacting to different dynamic scenarios and in suitably handling different situations. Furthermore, the usability and the practicality of the proposed software tool facilitate its deployment and customization to different production chains, and provide a practical example of the use of multi-agent systems and artificial intelligence in the context of industry 4.0. Full article
(This article belongs to the Special Issue Advances in Multi-Agent Systems)
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23 pages, 2975 KiB  
Article
Self-Adaptation of a Heterogeneous Swarm of Mobile Robots to a Covered Area
by Ján Zelenka, Tomáš Kasanický, Marek Bundzel and Rudolf Andoga
Appl. Sci. 2020, 10(10), 3562; https://0-doi-org.brum.beds.ac.uk/10.3390/app10103562 - 21 May 2020
Cited by 2 | Viewed by 2032
Abstract
An original swarm-based method for coordination of groups of mobile robots with a focus on the self-organization and self-adaptation of the groups is presented in this paper. The method is a nature-inspired decentralized algorithm that uses artificial pheromone marks and enables the cooperation [...] Read more.
An original swarm-based method for coordination of groups of mobile robots with a focus on the self-organization and self-adaptation of the groups is presented in this paper. The method is a nature-inspired decentralized algorithm that uses artificial pheromone marks and enables the cooperation of different types of independent reactive agents that operate in the air, on the ground, or in the water. The advantages of our solution include scalability, adaptability, and robustness. The algorithm worked with variable numbers of agents in the groups. It was resistant against failures of the individual robots. A transportation control algorithm that ensured the spreading of different types of agents across exploration space with different types of environments was introduced and tested. We established that our swarm control algorithm was able to successfully control three basic behaviors: space exploration, population management, and transportation. The behaviors were able to run simultaneously, and space exploration (the main goal) was never stopped or interrupted. All these features combined in a single algorithmic package represent a framework for future development of swarm-based agent systems applicable in a broad scope of environments. The results confirmed that the algorithm can be applied to monitoring, surveillance, patrolling, or search and rescue tasks. Full article
(This article belongs to the Special Issue Advances in Multi-Agent Systems)
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Review

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40 pages, 1345 KiB  
Review
Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
by Abdikarim Mohamed Ibrahim, Kok-Lim Alvin Yau, Yung-Wey Chong and Celimuge Wu
Appl. Sci. 2021, 11(22), 10870; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210870 - 17 Nov 2021
Cited by 8 | Viewed by 6169
Abstract
Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each [...] Read more.
Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL. Full article
(This article belongs to the Special Issue Advances in Multi-Agent Systems)
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