Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.3
1.9
Journals
Systems
Special Issues
Frontiers of Agents and Multiagent Systems
share announcement

Frontiers of Agents and Multiagent Systems

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 11619

Special Issue Editors

Dr. Fernando De la Prieta Pintado

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+I, 37007 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; cloud computing and distributed systems; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Rodriguez

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; distributed computing; machine learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan M. Corchado

E-Mail Website
Guest Editor
1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vicent Botti

E-Mail Website
Guest Editor
VRAIN Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 València, Spain
Interests: affective computing; agreement technology; artificial intelligence; computational chemistry; computer science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research on intelligent distributed systems has matured within the last decade and many effective applications are now being deployed. Technologies in distributed environments, such as multi-agent systems, the Internet of Things (IoT), wireless devices, Industry 4.0, etc. are increasing and are becoming an element of high added value and economic potential, both in industrial and research fields.

Most computing systems from personal laptops/computers to cluster/grid/cloud computing systems are available for parallel and distributed computing. Distributed computing performs an increasingly important role in modern signal/data processing, information fusion and electronics engineering (e.g., electronic commerce, mobile communications and wireless devices). Particularly, applying multi-agent systems in distributed environments is becoming an element of high added value and economic potential.

This Special Issue aims to advance the state-of-the-art application of intelligent methods to improve distributed scenarios, with a special focus on those applications that take practical applications of multi-Agent Systems, human collaboration, interactions, communication, or industrial scenarios into consideration. We especially encourage the submission of articles describing applications, but we also welcome theoretical work and review articles on novel applications of multi-agent systems and intelligent methods to distributed environments communications such as the Internet of Things (IoT), electronic commerce, mobile communications and wireless devices. 

Topics of interest include, but are not limited to practical applications of: 

  • Multi-agent Systems 
  • Distributed problem solving
  • Agent cooperation and negotiation
  • Human agent interaction, social networks, virtual communities
  • Reputation, trust, privacy and security
  • Agent engineering and development tools
  • Internet of Things, sensors and actuators
  • Big data and machine learning
  • Smart cities, smart homes, smart buildings, smart health, smart mobility and transportation
  • Semantic web, linked data
  • Agent-based simulation and prediction, social simulation

You may choose our Joint Special Issue in Robotics, or Joint Special Issue in Sensors.

Prof. Dr. Fernando De la Prieta
Prof. Dr. Sara Rodriguez
Prof. Dr. Juan M. Corchado
Prof. Dr. Vicent Botti
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. Systems 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 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.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 1479 KiB  
Article
Characterization and Costs of Integrating Blockchain and IoT for Agri-Food Traceability Systems
by Miguel Pincheira, Massimo Vecchio and Raffaele Giaffreda
Systems 2022, 10(3), 57; https://doi.org/10.3390/systems10030057 - 25 Apr 2022
Cited by 8 | Viewed by 3874
Abstract
An increasing amount of research focuses on integrating the Internet of Things and blockchain technology to address the requirements of traceability applications for Industry 4.0. However, there has been little quantitative analysis of several aspects of these new blockchain-based traceability systems. For instance, [...] Read more.
An increasing amount of research focuses on integrating the Internet of Things and blockchain technology to address the requirements of traceability applications for Industry 4.0. However, there has been little quantitative analysis of several aspects of these new blockchain-based traceability systems. For instance, very few works have studied blockchain’s impact on the resources of constrained IoT sensors. Similarly, the infrastructure costs of these blockchain-based systems are not widely understood. This paper characterizes the resources of low-cost IoT sensors and provides a monetary cost model for blockchain infrastructure to support blockchain-based traceability systems. First, we describe and implement a farm-to-fork case study using public and private blockchain networks. Then, we analyze the impact of blockchain in six different resource-limited IoT devices in terms of disk and memory footprint, processing time, and energy consumption. Next, we present an infrastructure cost model and use it to identify the costs for the public and private networks. Finally, we evaluate the traceability of a product in different scenarios. Our results showed that low-cost sensors could directly interact with both types of blockchains with minimal energy overhead. Furthermore, our cost model showed that setting a private blockchain infrastructure costs approximately the same as that managing 50 products on a public blockchain network. Full article
(This article belongs to the Special Issue Frontiers of Agents and Multiagent Systems)
►▼ Show Figures

Figure 1

17 pages, 778 KiB  
Article
Machine Reading at Scale: A Search Engine for Scientific and Academic Research
by Norberto Sousa, Nuno Oliveira and Isabel Praça
Systems 2022, 10(2), 43; https://doi.org/10.3390/systems10020043 - 05 Apr 2022
Cited by 2 | Viewed by 3250
Abstract
The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as [...] Read more.
The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance. Full article
(This article belongs to the Special Issue Frontiers of Agents and Multiagent Systems)
►▼ Show Figures

Figure 1

24 pages, 994 KiB  
Article
“I’m Afraid I Can’t Do That, Dave”; Getting to Know Your Buddies in a Human–Agent Team
by Maarten P. D. Schadd, Tjeerd A. J. Schoonderwoerd, Karel van den Bosch, Olaf H. Visker, Tjalling Haije and Kim H. J. Veltman
Systems 2022, 10(1), 15; https://doi.org/10.3390/systems10010015 - 12 Feb 2022
Cited by 3 | Viewed by 2915
Abstract
The rapid progress in artificial intelligence enables technology to more and more become a partner of humans in a team, rather than being a tool. Even more than in human teams, partners of human–agent teams have different strengths and weaknesses, and they must [...] Read more.
The rapid progress in artificial intelligence enables technology to more and more become a partner of humans in a team, rather than being a tool. Even more than in human teams, partners of human–agent teams have different strengths and weaknesses, and they must acknowledge and utilize their respective capabilities. Coordinated team collaboration can be accomplished by smartly designing the interactions within human–agent teams. Such designs are called Team Design Patterns (TDPs). We investigated the effects of a specific TDP on proactive task reassignment. This TDP supports team members to dynamically allocate tasks by utilizing their knowledge about the task demands and about the capabilities of team members. In a pilot study, agent–agent teams were used to study the effectiveness of proactive task reassignment. Results showed that this TDP improves a team’s performance, provided that partners have accurate knowledge representations of each member’s skill level. The main study of this paper addresses the effects of task reassignments in a human–agent team. It was hypothesized that when agents provide explanations when issuing and responding to task reassignment requests, this will enhance the quality of the human’s mental model. Results confirmed that participants developed more accurate mental models when agent-partners provide explanations. This did not result in a higher performance of the human–agent team, however. The study contributes to our understanding of designing effective collaboration in human–agent teams. Full article
(This article belongs to the Special Issue Frontiers of Agents and Multiagent Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop