Special Issue "Application of Multiagent Systems and Artificial Intelligence Techniques in Aviation"

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (30 July 2018)

Special Issue Editors

Guest Editor
Dr. Alexei Sharpanskykh

Air Transport and Operations, Delft University of Technology, Faculty of Aerospace Engineering, Kluyverweg 1, 2629 HS Delft, The Netherlands
Website | E-Mail
Interests: mathematical and computational modeling and analysis of safety and resilience of complex sociotechnical systems in aviation; application of multiagent systems and artificial intelligence techniques in aviation; exploration and development of analysis methods (for example, model checking techniques) and tools for complex sociotechnical systems; development of tools and techniques for simulation of sociotechnical systems
Guest Editor
Dr. António J.M. Castro

LIACC (Laboratory of Artificial Intelligence and Computer Science), University of Porto, 4099-002 Porto, Portugal
Website | E-Mail
Interests: distributed systems; multi-agent systems in general; organization structure in distributed systems/MAS; agent oriented software engineering; intelligent user interfaces; learning (machine learning); evolutionary computing; autonomy; MAS and agents in aerospace; disruption management in airline/airport operations, space operations and air traffic control

Special Issue Information

Dear Colleagues,

Methods and tools from the areas of multiagent systems (MAS) and artificial intelligence (AI) have been gaining more and more popularity in aerospace. Next to current, highly-popular Big Data and machine learning techniques stemming from statistical AI, approaches from symbolic AI, based on rules, ontologies, mathematical logics, and formal reasoning are also applied in diverse areas of aerospace, such ATM, aircraft design, airport operations, maintenance, swarming of satellites, and UAS/UAV. A new direction of multiagent organizations and agent-based modelling and simulation (ABMS) of air transport and space operations, which includes interaction between humans and technical systems, is also growing in popularity.

The techniques, methods, and tools in AI, and MAS, and ABMS in particular, advance rapidly with every passing year, thus opening up new opportunities for diverse engineering applications in airspace. AI- and MAS-based solutions have repeatedly demonstrated more robustness, flexibility, and scalability than more traditional top-down approaches. However, the full potential of these novel techniques in application to airspace is to be determined.

This Special Issue welcomes a whole range of contributions, in which AI, MAS, and ABMS techniques are developed and/or applied to aerospace.

Topics of interest include, but are not limited to:

  • Autonomous agents and multiagent systems in aerospace applications
  • Knowledge representation, reasoning, and logic in aerospace applications
  • Agent-based modelling and simulation of sociotechnical systems in aerospace
  • Robotics, perception, and vision in aerospace applications
  • Big Data, machine learning, and data mining in aerospace applications
  • Planning and scheduling in air transport
  • Industrial aerospace applications of AI, MAS and ABMS

Dr. Alexei Sharpanskykh
Dr. António J.M. Castro
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. Aerospace 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 550 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)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Open AccessArticle Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification
Aerospace 2018, 5(4), 103; https://doi.org/10.3390/aerospace5040103
Received: 6 August 2018 / Revised: 21 September 2018 / Accepted: 26 September 2018 / Published: 1 October 2018
PDF Full-text (2839 KB) | HTML Full-text | XML Full-text
Abstract
Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human–machine interface (HMI), configured via [...] Read more.
Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human–machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems. Full article
Figures

Graphical abstract

Open AccessArticle Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time
Aerospace 2018, 5(4), 101; https://doi.org/10.3390/aerospace5040101
Received: 4 September 2018 / Revised: 27 September 2018 / Accepted: 29 September 2018 / Published: 30 September 2018
PDF Full-text (1673 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational [...] Read more.
In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. Thus, we used a developed complexity metric to evaluate the actual boarding progress and a machine learning approach to predict the final boarding time during running operations. A validated passenger boarding model is used to provide reliable aircraft status data, since no operational data are available today. These data are aggregated to a time-based complexity value and used as input for our recurrent neural network approach for predicting the boarding progress. In particular we use a Long Short-Term Memory model to learn the dynamical passenger behavior over time with regards to the given complexity metric. Full article
Figures

Graphical abstract

Open AccessArticle Field Trial Measurements to Validate a Stochastic Aircraft Boarding Model
Received: 2 February 2018 / Revised: 1 March 2018 / Accepted: 5 March 2018 / Published: 7 March 2018
Cited by 11 | PDF Full-text (3547 KB) | HTML Full-text | XML Full-text
Abstract
Efficient boarding procedures have to consider both operational constraints and the individual passenger behavior. In contrast to the aircraft handling processes of fueling, catering and cleaning, the boarding process is more driven by passengers than by airport or airline operators. This paper delivers [...] Read more.
Efficient boarding procedures have to consider both operational constraints and the individual passenger behavior. In contrast to the aircraft handling processes of fueling, catering and cleaning, the boarding process is more driven by passengers than by airport or airline operators. This paper delivers a comprehensive set of operational data including classification of boarding times, passenger arrival times, times to store hand luggage, and passenger interactions in the aircraft cabin as a reliable basis for calibrating models for aircraft boarding. In this paper, a microscopic approach is used to model the passenger behavior, where the passenger movement is defined as a one-dimensional, stochastic, and time/space discrete transition process. This model is used to compare measurements from field trials of boarding procedures with simulation results and demonstrates a deviation smaller than 5%. Full article
Figures

Graphical abstract

Aerospace EISSN 2226-4310 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top