Special Issue "Multi-Agent Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computer Science and Electrical Engineering".

Deadline for manuscript submissions: 31 December 2017

Special Issue Editors

Guest Editor
Prof. Dr. Vicent Botti

Department of Information Systems and Computation (DSIC), Universitat Politécnica de València, Valencia 46022, Spain
Website | E-Mail
Interests: artificial intelligence; multiagent systems; agreement technologies; agent-based simulation; affective agents
Guest Editor
Prof. Dr. Andrea Omicini

Department of Computer Science & Engineering, Alma Mater Studiorum-Università di Bologna, Bologna 40126, Italy
Website | E-Mail
Interests: artificial intelligence; software engineering; autonomous systems; programming languages; distributed systems
Co-Guest Editor
Dr. Stefano Mariani

DISMI, Università degli Studi di Modena e Reggio Emilia, Modena 41121, Italy
Website | E-Mail
Interests: coordination models; languages & technologies; expressiveness of coordination languages; nature-inspired algorithms; self-organi
Co-Guest Editor
Dr. Vicente Julian

Department of Information Systems and Computation (DSIC), Universitat Politécnica de València, Valencia 46022, Spain
Website | E-Mail
Interests: multi-agent systems; agent architectures; multi-agent system methodologies; real-time agents

Special Issue Information

Dear Colleagues,

Research work done on Intelligent Agents and Multi-Agent Systems has matured during the last decade, and many effective applications of this technology are currently deployed. Despite the fact that computational approaches for multi-agent systems have mainly emerged in the last 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 have 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.

The interest of researchers in this new discipline lies in diverse reasons. 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 analyzing different aspects, as well as its possible application to various domains. In this review of the current state, it 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. Vicent Botti
Dr. Vicente Julian
Prof. 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 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 1200 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

  • Biologically-inspired approaches and methods

  • Collective intelligence

  • Complex systems

  • Distributed problem solving

  • Human-robot/agent interaction

  • Intelligent control and manufacturing systems

  • Learning and adaptation in MAS

  • Methodologies for agent-based systems

  • Multi-robot systems

  • Negotiation and conflict resolution

  • Normative systems

  • Organizations and institutions

  • Reasoning in agent-based systems

  • Self-organization

  • Single and multi-agent planning and scheduling

  • Socio-technical systems

  • Teamwork, team formation, teamwork analysis

  • Trust and reputation

Published Papers (4 papers)

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Research

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Open AccessArticle ABS-SOCI: An Agent-Based Simulator of Student Sociograms
Appl. Sci. 2017, 7(11), 1126; doi:10.3390/app7111126
Received: 22 September 2017 / Revised: 15 October 2017 / Accepted: 27 October 2017 / Published: 1 November 2017
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Abstract
Sociograms can represent the social relations between students. Some kinds of sociograms are more suitable than others for achieving a high academic performance of students. However, for now, at the beginning of an educative period, it is not possible to know for sure
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Sociograms can represent the social relations between students. Some kinds of sociograms are more suitable than others for achieving a high academic performance of students. However, for now, at the beginning of an educative period, it is not possible to know for sure how the sociogram of a group of students will be or evolve during a semester or an academic year. In this context, the current approach presents an Agent-Based Simulator (ABS) that predicts the sociogram of a group of students taking into consideration their psychological profiles, by evolving an initial sociogram through time. This simulator is referred to as ABS-SOCI (ABS for SOCIograms). For instance, this can be useful for organizing class groups for some subjects of engineering grades, anticipating additional learning assistance or testing some teaching strategies. As experimentation, ABS-SOCI has been executed 100 times for each one of four real scenarios. The results show that ABS-SOCI produces sociograms similar to the real ones considering certain sociometrics. This similarity has been corroborated by statistical binomial tests that check whether there are significant differences between the simulations and the real cases. This experimentation also includes cross-validation and an analysis of sensitivity. ABS-SOCI is free and open-source to (1) ensure the reproducibility of the experiments; (2) to allow practitioners to run simulations; and (3) to allow developers to adapt the simulator for different environments. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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Open AccessArticle Agent-Based Model for Automaticity Management of Traffic Flows across the Network
Appl. Sci. 2017, 7(9), 928; doi:10.3390/app7090928
Received: 1 July 2017 / Revised: 9 August 2017 / Accepted: 4 September 2017 / Published: 9 September 2017
Cited by 1 | PDF Full-text (3403 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an agent-based model that performs the management of traffic flows in a network with the purpose of observing in a simulation of distinctive congestion scenarios how the automation of the monitoring task improves the network performance. The model implements a
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This paper presents an agent-based model that performs the management of traffic flows in a network with the purpose of observing in a simulation of distinctive congestion scenarios how the automation of the monitoring task improves the network performance. The model implements a decision-making algorithm to determine the path that the data flows will follow to reach their destination, according to the results of the negotiation between the agents. In addition, we explain how the behavior of the network is affected by its topology. The aim of this paper is to propose an agent-based model that simplifies the management of the traffic flows in a communications network towards the automaticity of the system. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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Open AccessArticle Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
Appl. Sci. 2017, 7(9), 903; doi:10.3390/app7090903
Received: 14 July 2017 / Revised: 21 August 2017 / Accepted: 30 August 2017 / Published: 4 September 2017
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Abstract
Navigating a robot in a dynamic environment is a challenging task, especially when the behavior of other agents such as pedestrians, is only partially predictable. Also, the kinodynamic constraints on robot motion add an extra challenge. This paper proposes a novel navigational strategy
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Navigating a robot in a dynamic environment is a challenging task, especially when the behavior of other agents such as pedestrians, is only partially predictable. Also, the kinodynamic constraints on robot motion add an extra challenge. This paper proposes a novel navigational strategy for collision avoidance of a kinodynamically constrained robot from multiple moving passive agents with partially predictable behavior. Specifically, this paper presents a new approach to identify the set of control inputs to the robot, named control obstacle, which leads it towards a collision with a passive agent moving along an arbitrary path. The proposed method is developed by generalizing the concept of nonlinear velocity obstacle (NLVO), which is used to avoid collision with a passive agent, and takes into account the kinodynamic constraints on robot motion. Further, it formulates the navigational problem as an optimization problem, which allows the robot to make a safe decision in the presence of various sources of unmodelled uncertainties. Finally, the performance of the algorithm is evaluated for different parameters and is compared to existing velocity obstacle-based approaches. The simulated experiments show the excellent performance of the proposed approach in term of computation time and success rate. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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Other

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Open AccessTechnical Note On the Delayed Scaled Consensus Problems
Appl. Sci. 2017, 7(7), 713; doi:10.3390/app7070713
Received: 28 May 2017 / Revised: 17 June 2017 / Accepted: 6 July 2017 / Published: 11 July 2017
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Abstract
In this note, we study the scaled consensus (tracking) problems, wherein all agents reach agreement, but with different assigned ratios in the asymptote. Based on the nearest neighbor-interaction rules, the scaled consensus processes are characterized with and without time delay. We consider both
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In this note, we study the scaled consensus (tracking) problems, wherein all agents reach agreement, but with different assigned ratios in the asymptote. Based on the nearest neighbor-interaction rules, the scaled consensus processes are characterized with and without time delay. We consider both the signal transmission and signal processing delays and calculate the final scaled consensus values. When the underlying communication network contains a spanning tree, it is found that the scaled consensus can be achieved independent of the transmission delays while the specified consensus values in the asymptote depend on the initial history of the agents over a period of time. This phenomenon is in sharp contrast to the case of processing delays, where large delays are likely to jeopardize the consensus behavior, but the scaled consensus values once achieved are the same as the undelayed case. Full article
(This article belongs to the Special Issue Multi-Agent Systems)
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