Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.1
2.6
Journals
Machines
Special Issues
Optimization and AI of Autonomous Multi-Agents
share announcement

Optimization and AI of Autonomous Multi-Agents

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2186

Special Issue Editor

Dr. Chansoo Kim

E-Mail Website
Guest Editor
Computational Science Research Center, Korea Institute of Science and Technology (KIST), University of Science and Technology (UST), Seoul 02792, Republic of Korea
Interests: fundamentals and mathematics of machine learning and AI; explainable and causal AI as well as ML; map matching; optimizations; optimal policies in multi-agent systems; economics and finance in AI; heavy tails; complex systems

Special Issue Information

Dear Colleagues,

We have experienced the power and wide as well as quick applications of AI during the past decade. It highly influences the field of autonomous control and optimizations. We are expecting to introduce current research in the fields of AI and machine learning for applications of autonomous multi-agents, which include various levels of autonomous controls from the agent to system-wide level. One of the important examples is autonomous vehicles at the agent level, while the emergence of system behavior arises from their interactions.

We are at the cross-section of the state of the art in these fields as well as recent developments in AI, machine learning, and optimization schemes. Readers may be interested in the following aspects:

  • Literature survey.
  • Optimization and emergence in autonomous multi-agent systems.
  • Control and design of autonomous agents, such as vehicles.
  • AI and machine learning in autonomous multi-agent systems.
  • Case studies.
  • Sensors, the IoT, and smart things.
  • Big data analytics in these fields.
  • Complexity and complex systems.
  • Emergent and autonomous behaviors.

The Special Issue is not limited to the above, and welcomes various applications as well as subjects.

Dr. Chansoo Kim
Guest Editor

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. Machines 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 (2 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

22 pages, 1358 KiB  
Article
Adaptive Sliding Mode Resilient Control of Multi-Robot Systems with a Leader–Follower Model under Byzantine Attacks in the Context of the Industrial Internet of Things
by Muhammad Nasir and Ananda Maiti
Machines 2024, 12(3), 205; https://doi.org/10.3390/machines12030205 - 20 Mar 2024
Viewed by 763
Abstract
In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a [...] Read more.
In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a Byzantine attack. In the start, a finite-time consensus control mechanism is proposed to identify the essential conditions required for ensuring consensus accuracy in multi-robot systems, even when faced with Byzantine attacks using Lyapunov theory. Subsequently, a sliding mode control is combined with an adaptive technique for multi-robot systems that lack prior knowledge of Byzantine attack. Later, an attack observer is proposed to estimate the performance of multi-robot systems in the presence of a Byzantine attack. Additionally, chattering effects are mitigated by employing integral sliding mode control. As a result, resilient consensus performance of multi-robot systems can be achieved in a finite time interval. A simulation example is also presented to validate the effectiveness of the proposed model. Furthermore, we delve into the data structure of the proposed method and explore its integration with Artificial Intelligence for seamless incorporation into the Industrial Internet of Things applications. Full article
(This article belongs to the Special Issue Optimization and AI of Autonomous Multi-Agents)
►▼ Show Figures

Figure 1

20 pages, 1484 KiB  
Article
Reinforcement Learning-Based Dynamic Zone Positions for Mixed Traffic Flow Variable Speed Limit Control with Congestion Detection
by Filip Vrbanić, Martin Gregurić, Mladen Miletić and Edouard Ivanjko
Machines 2023, 11(12), 1058; https://doi.org/10.3390/machines11121058 - 28 Nov 2023
Viewed by 953
Abstract
Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance [...] Read more.
Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from 10% to 100% and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario. Full article
(This article belongs to the Special Issue Optimization and AI of Autonomous Multi-Agents)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop