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Applied Sciences
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New Insights in Multi-Agent Systems Cooperation, Control and Optimisation
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New Insights in Multi-Agent Systems Cooperation, Control and Optimisation

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 March 2023) | Viewed by 5009

Special Issue Editors

Dr. Marco Pérez-Hernández

E-Mail Website
Guest Editor
Department of Computer Science and Creative Technologies, University of the West of England (UWE), Bristol, UK
Interests: agent-based software architectures; autonomous and adaptable systems; decentralised and constrained decision-making processes; industrial IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Manuel Herrera

E-Mail Website
Guest Editor
Department of Engineering, University of Cambridge, Cambridge, UK
Interests: water systems; time series analysis; network science
Special Issues, Collections and Topics in MDPI journals
Dr. Rakib Abdur

E-Mail Website
Guest Editor
Department of Computer Science and Creative Technologies, University of the West of England (UWE), Bristol, UK
Interests: multi-agent systems; context-aware systems; logics and formal verification; semantic web and ontology-driven systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Hong

E-Mail Website
Guest Editor
Department of Computer Science and Creative Technologies, University of the West of England (UWE), Bristol, UK
Interests: graph mining; social network analysis; intelligent autonomous systems; AI planning; BDI-based multi-agent systems

Special Issue Information

Dear Colleagues,

The intrinsic social nature of many natural and artificial phenomena emphasizes the need to approach multiple open problems in science and engineering as multi-agent systems. After more than four decades of laying the foundations of distributed artificial intelligence and multi-agent systems, critical challenges for the understanding and engineering of these systems remain unsolved.

The aim of this Special Issue is to attract novel, high-quality contributions on the theory, modelling, engineering, and applications of the social processes of multi-agent systems. This includes contributions studying challenges related to collective control, collective decision-making and optimisation, collective and social machine learning, multi-agent reinforcement learning, as well as the coordination, cooperation, and evolution of agents, etc. We invite manuscripts covering the representation, modelling, and simulation of structural, dynamic, social, and network-related aspects of complex natural and artificial multi-agent systems. We also seek papers addressing the design, validation, verification, deployment, and explainability of the social methods, mechanisms, architectures, techniques, and algorithms for the engineering of multi-agent systems. The problems and solutions presented may be inspired by multiple disciplines and may have applications in any area, for example, healthcare, climate studies, smart cities, critical infrastructures, industry 4.0, internet of things, multi-robot teams, or digital twins.

Dr. Marco Pérez-Hernández
Dr. Manuel Herrera
Dr. Rakib Abdur
Dr. Jun Hong
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.

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Published Papers (2 papers)

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Research

12 pages, 415 KiB  
Article
A Deep Reinforcement Learning Quality Optimization Framework for Multimedia Streaming over 5G Networks
by Alberto del Río, Javier Serrano, David Jimenez, Luis M. Contreras and Federico Alvarez
Appl. Sci. 2022, 12(20), 10343; https://doi.org/10.3390/app122010343 - 14 Oct 2022
Cited by 7 | Viewed by 2224
Abstract
Media applications are amongst the most demanding services. They require high amounts of network capacity as well as computational resources for synchronous high-quality audio–visual streaming. Recent technological advances in the domain of new generation networks, specifically network virtualization and Multiaccess Edge Computing (MEC) [...] Read more.
Media applications are amongst the most demanding services. They require high amounts of network capacity as well as computational resources for synchronous high-quality audio–visual streaming. Recent technological advances in the domain of new generation networks, specifically network virtualization and Multiaccess Edge Computing (MEC) have unlocked the potential of the media industry. They enable high-quality media services through dynamic and efficient resource allocation taking advantage of the flexibility of the layered architecture offered by 5G. The presented work demonstrates the potential application of Artificial Intelligence (AI) capabilities for multimedia services deployment. The goal was targeted to optimize the Quality of Experience (QoE) of real-time video using dynamic predictions by means of Deep Reinforcement Learning (DRL) algorithms. Specifically, it contains the initial design and test of a self-optimized cloud streaming proof-of-concept. The environment is implemented through a virtualized end-to-end architecture for multimedia transmission, capable of adapting streaming bitrate based on a set of actions. A prediction algorithm is trained through different state conditions (QoE, bitrate, encoding quality, and RAM usage) that serves the optimizer as the encoding values of the environment for action prediction. Optimization is applied by selecting the most suitable option from a set of actions. These consist of a collection of predefined network profiles with associated bitrates, which are validated by a list of reward functions. The optimizer is built employing the most prominent algorithms in the DRL family, with the use of two Neural Networks (NN), named Advantage Actor–Critic (A2C). As a result of its application, the ratio of good quality video segments increased from 65% to 90%. Furthermore, the number of image artifacts is reduced compared to standard sessions without applying intelligent optimization. From these achievements, the global QoE obtained is clearly better. These results, based on a simulated scenario, increase the interest in further research on the potential of applying intelligence to enhance the provisioning of media services under real conditions. Full article
(This article belongs to the Special Issue New Insights in Multi-Agent Systems Cooperation, Control and Optimisation)
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36 pages, 4033 KiB  
Article
Development and Comparison of Ten Differential-Evolution and Particle Swarm-Optimization Based Algorithms for Discount-Guaranteed Ridesharing Systems
by Fu-Shiung Hsieh
Appl. Sci. 2022, 12(19), 9544; https://doi.org/10.3390/app12199544 - 23 Sep 2022
Cited by 11 | Viewed by 1944
Abstract
Savings on transportation costs provide an important incentive for shared mobility models in smart cities. Therefore, the problem of maximizing cost savings has been extensively studied in the ridesharing literature. Most studies on ridesharing focus on the maximization of the overall savings on [...] Read more.
Savings on transportation costs provide an important incentive for shared mobility models in smart cities. Therefore, the problem of maximizing cost savings has been extensively studied in the ridesharing literature. Most studies on ridesharing focus on the maximization of the overall savings on transportation costs. However, the maximization of the overall savings on transportation costs may satisfy users’ expectations for cost savings. For people to adopt ridesharing as a means to reduce costs, a minimal expected cost savings discount must be offered. There is obviously a gap between the existing studies and the real problems faced by service providers. This calls for the development of a study to formulate a ridesharing model that guarantees the satisfaction of a minimal expected cost savings discount. In this paper, we considered a discount-guaranteed ridesharing model that ensures the provision of a minimal expected cost savings discount to ridesharing participants to improve users’ satisfaction with the ridesharing service in terms of cost savings. The goal was to maximize the overall cost savings under certain capacity, spatial, and time constraints and the constraint that the discount offered to ridesharing participants could be no lower than the minimal expected cost savings discount. Due to the complexity of the optimization problem, we adopted two evolutionary computation approaches, differential evolution and particle swarm optimization, to develop ten algorithms for solving the problem. We illustrated the proposed method by an example. The results indicated that the proposed method could guarantee that the discount offered to ridesharing participants was greater than or equal to the minimal expected cost savings discount. We also conducted two series of experiments to assess the performance and efficiency of the different solution algorithms. We analyzed the results to provide suggestions for selecting the appropriate solution algorithm based on its performance and efficiency. Full article
(This article belongs to the Special Issue New Insights in Multi-Agent Systems Cooperation, Control and Optimisation)
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