Special Issue "Latest Artificial Intelligence Research Output 2018"

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (30 April 2019).

Special Issue Editor

Guest Editor
Prof. Dr. Yin-Fu Huang

Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliu, Yunlin, Taiwan
Website | E-Mail
Interests: database systems; multimedia systems; data mining; big data analytics

Special Issue Information

Dear Colleagues,

The Artificial Intelligence International Conference—A2IC conference aims to establish an international forum of reference for the latest advances in the field of Artificial Intelligence (AI). This first edition aims at joining both academy and industry by covering not only fundamental and applied research, but also philosophical and ethical issues regarding the future of humanity in an AI world.

Prof. Dr. Yin-Fu Huang
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 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. Robotics is an international peer-reviewed open access quarterly 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 1000 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

  • Machine learning
  • Neural networks and deep learning
  • Pattern recognition
  • Knowledge representation
  • Ontologies and Semantic Web
  • Fuzzy logic and fuzzy systems
  • Multiagent systems
  • Natural language processing
  • Robotics
  • Computer vision and perception
  • Cognitive systems
  • Computational creativity
  • Bioinformatics
  • Biometric authentication

Published Papers (3 papers)

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

Research

Jump to: Review

Open AccessArticle
A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment
Received: 10 May 2019 / Revised: 1 July 2019 / Accepted: 1 July 2019 / Published: 4 July 2019
PDF Full-text (3428 KB) | HTML Full-text | XML Full-text
Abstract
Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine learning (ML) techniques have been widely used as a [...] Read more.
Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors vulnerable to a range of adversarial attacks. Thus, this paper surveys the attacks against Twitter spam detectors in an adversarial environment, and a general taxonomy of potential adversarial attacks is presented using common frameworks from the literature. Examples of adversarial activities on Twitter that were discovered after observing Arabic trending hashtags are discussed in detail. A new type of spam tweet (adversarial spam tweet), which can be used to undermine a deployed classifier, is examined. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors to such attacks are investigated. Full article
(This article belongs to the Special Issue Latest Artificial Intelligence Research Output 2018)
Figures

Figure 1

Open AccessArticle
Long-Term Adaptivity in Distributed Intelligent Systems: Study of ViaBots in a Simulated Environment
Received: 28 February 2019 / Revised: 20 March 2019 / Accepted: 26 March 2019 / Published: 29 March 2019
PDF Full-text (3715 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a long-term adaptive distributed intelligent systems model which combines an organization theory and multi-agent paradigm—ViaBots. Currently, the need for adaptivity in autonomous intelligent systems becomes crucial due to the increase in the complexity and diversity of the tasks that autonomous [...] Read more.
This paper proposes a long-term adaptive distributed intelligent systems model which combines an organization theory and multi-agent paradigm—ViaBots. Currently, the need for adaptivity in autonomous intelligent systems becomes crucial due to the increase in the complexity and diversity of the tasks that autonomous robots are employed for. To deal with the design complexity of such systems within the ViaBots model, each part of the modeled system is designed as an autonomous agent and the entire model, as a multi-agent system. Based on the viable system model, which is widely used to ensure viability, (i.e., long-term autonomy of organizations), the ViaBots model defines the necessary roles a system must fulfill to be capable to adapt both to changes in its environment (like changes in the task) and changes within the system itself (like availability of a particular robot). Along with static role assignments, ViaBots propose a mechanism for role transition from one agent to another as one of the key elements of long term adaptivity. The model has been validated in a simulated environment using an example of a conveyor system. The simulated model enabled the multi-robot system to adapt to the quantity and characteristics of the available robots, as well as to the changes in the parts to be processed by the system. Full article
(This article belongs to the Special Issue Latest Artificial Intelligence Research Output 2018)
Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Heterogeneous Map Merging: State of the Art
Received: 24 April 2019 / Revised: 14 August 2019 / Accepted: 15 August 2019 / Published: 20 August 2019
PDF Full-text (1203 KB)
Abstract
Multi-robot mapping and environment modeling have several advantages that make
it an attractive alternative to the mapping with a single robot: faster exploration, higher
fault tolerance, richer data due to different sensors being used by different systems. However,
the environment modeling with several [...] Read more.
Multi-robot mapping and environment modeling have several advantages that make
it an attractive alternative to the mapping with a single robot: faster exploration, higher
fault tolerance, richer data due to different sensors being used by different systems. However,
the environment modeling with several robotic systems operating in the same area causes problems
of higher-order—acquired knowledge fusion and synchronization over time, revealing the same
environment properties using different sensors with different technical specifications. While the
existing robot map and environment model merging techniques allow merging certain homogeneous
maps, the possibility to use sensors of different physical nature and different mapping algorithms is
limited. The resulting maps from robots with different specifications are heterogeneous, and even
though some research on how to merge fundamentally different maps exists, it is limited to specific
applications. This research reviews the state of the art in homogeneous and heterogeneous map
merging and illustrates the main research challenges in the area. Six factors are identified that
influence the outcome of map merging: (1) robotic platform hardware configurations, (2) map
representation types, (3) mapping algorithms, (4) shared information between robots, (5) relative
positioning information, (6) resulting global maps. Full article
(This article belongs to the Special Issue Latest Artificial Intelligence Research Output 2018)
Robotics EISSN 2218-6581 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top