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Artificial Intelligence for Decision Making
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Artificial Intelligence for Decision Making

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 8291

Special Issue Editors

Dr. Malik Haddad

E-Mail Website
Guest Editor
New College of the Humanities, Northeastern University, London E1W 1LP, UK
Interests: assistive technology; ultrasonic; collision avoidance; artificial neural networks (ANN); AI; expert systems; human machine interface; decision making; multiple criteria decision making (MCDM), project management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. David Sanders

E-Mail Website
Co-Guest Editor
School of Mechanical and Design Engineering, Faculty of Technology, University of Portsmouth, Portsmouth PO1 3DJ, UK
Interests: automation; computing & electronics; design

Special Issue Information

Dear Colleagues,

Artificial Intelligence techniques are increasingly extending and enriching decision support through such means as coordinating data delivery, analyzing data trends, providing forecasts, developing data consistency, quantifying uncertainty, anticipating the user’s data needs, providing information to the user in the most appropriate forms, and suggesting courses of action. This session of the 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems focuses on the use of Artificial Intelligence to enhance decision making.

The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in the design of intelligent group decision support systems that use AI techniques, such as machine learning, Bayesian networks, neural networks, fuzzy logic, and others, to improve and enhance support for decision makers in solving difficult applied problems that involve large amounts of data, are often real time and benefit from complex reasoning.

Dr. Malik Haddad
Prof. Dr. David Sanders
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. Sensors 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 2600 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

  • decision support system
  • artificial intelligence
  • intelligent decision making and applications
  • machine learning in decision making

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Published Papers (2 papers)

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Research

15 pages, 1906 KiB  
Article
A Knowledge-Grounded Task-Oriented Dialogue System with Hierarchical Structure for Enhancing Knowledge Selection
by Hayoung Lee and Okran Jeong
Sensors 2023, 23(2), 685; https://doi.org/10.3390/s23020685 - 6 Jan 2023
Cited by 5 | Viewed by 3925
Abstract
For a task-oriented dialogue system to provide appropriate answers to and services for users’ questions, it is necessary for it to be able to utilize knowledge related to the topic of the conversation. Therefore, the system should be able to select the most [...] Read more.
For a task-oriented dialogue system to provide appropriate answers to and services for users’ questions, it is necessary for it to be able to utilize knowledge related to the topic of the conversation. Therefore, the system should be able to select the most appropriate knowledge snippet from the knowledge base, where external unstructured knowledge is used to respond to user requests that cannot be solved by the internal knowledge addressed by the database or application programming interface. Therefore, this paper constructs a three-step knowledge-grounded task-oriented dialogue system with knowledge-seeking-turn detection, knowledge selection, and knowledge-grounded generation. In particular, we propose a hierarchical structure of domain-classification, entity-extraction, and snippet-ranking tasks by subdividing the knowledge selection step. Each task is performed through the pre-trained language model with advanced techniques to finally determine the knowledge snippet to be used to generate a response. Furthermore, the domain and entity information obtained because of the previous task is used as knowledge to reduce the search range of candidates, thereby improving the performance and efficiency of knowledge selection and proving it through experiments. Full article
(This article belongs to the Special Issue Artificial Intelligence for Decision Making)
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17 pages, 1282 KiB  
Article
Visual Pretraining via Contrastive Predictive Model for Pixel-Based Reinforcement Learning
by Tung M. Luu, Thang Vu, Thanh Nguyen and Chang D. Yoo
Sensors 2022, 22(17), 6504; https://doi.org/10.3390/s22176504 - 29 Aug 2022
Cited by 5 | Viewed by 2738
Abstract
In an attempt to overcome the limitations of reward-driven representation learning in vision-based reinforcement learning (RL), an unsupervised learning framework referred to as the visual pretraining via contrastive predictive model (VPCPM) is proposed to learn the representations detached from the policy learning. Our [...] Read more.
In an attempt to overcome the limitations of reward-driven representation learning in vision-based reinforcement learning (RL), an unsupervised learning framework referred to as the visual pretraining via contrastive predictive model (VPCPM) is proposed to learn the representations detached from the policy learning. Our method enables the convolutional encoder to perceive the underlying dynamics through a pair of forward and inverse models under the supervision of the contrastive loss, thus resulting in better representations. In experiments with a diverse set of vision control tasks, by initializing the encoders with VPCPM, the performance of state-of-the-art vision-based RL algorithms is significantly boosted, with 44% and 10% improvement for RAD and DrQ at 100 steps, respectively. In comparison to the prior unsupervised methods, the performance of VPCPM matches or outperforms all the baselines. We further demonstrate that the learned representations successfully generalize to the new tasks that share a similar observation and action space. Full article
(This article belongs to the Special Issue Artificial Intelligence for Decision Making)
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