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Algorithms
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Self-Learning and Self-Adapting Algorithms in Machine Learning
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Self-Learning and Self-Adapting Algorithms in Machine Learning

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 8200

Special Issue Editors

Dr. Patrizia Ribino

E-Mail Website
Guest Editor
Istituto Di Calcolo E Reti Ad Alte Prestazioni, 87036 Rende, Italy
Interests: cognitive and normative systems; software engineering; multi-agent systems; ontologies and knowledge formalization; reinforcement learning; artificial intelligence
Dr. Giovanni Paragliola

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking, National Research Council of Italy, 80131 Naples, Italy
Interests: federated learning; deep learning; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI techniques in data models help deal with uncertainties when process models are challenging to obtain. However, considering that either the system or its environment may evolve, the nature and the distribution of the data may change, leading to data model inaccuracy. When this happens, AI techniques can be used to adapt the data model; the challenge then is maintaining an accurate data model.

The development of such systems is advanced over time by AI technologies such as Reinforcement Learning, Continuous Learning, Deep Learning and Machine Learning.

Nowadays, many application fields, such as gaming, finance, banking, autonomous vehicles, healthcare and robotics, are benefiting from the adoption of this paradigm.

This Special Issue aims to bring together the latest achievements and breakthroughs by academia and industry in self-learning and self-adapting systems.

We welcome papers detailing any new method or development of well-known methods that improve the performance, scalability or generalization of such solutions. Successful applications of self-learning and self-adapting methods are also of interest.

Subtopics of interest include, but are not limited to:

  • Reinforcement Learning
  • multi-agent Reinforcement Learning;
  • Deep Learning over time;
  • self-adapting systems;
  • time series forecasting;
  • active Machine Learning
  • adaptive normative multi-agent systems

Application fields: Automotive, Robotics, Healthcare, Finance, Gaming, Business management, Resource management, IoT and Industry 4.0

Dr. Patrizia Ribino
Dr. Giovanni Paragliola
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. Algorithms 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 1600 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 (4 papers)

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Research

21 pages, 539 KiB  
Article
SmartBuild RecSys: A Recommendation System Based on the Smart Readiness Indicator for Energy Efficiency in Buildings
by Muhammad Talha Siddique, Paraskevas Koukaras, Dimosthenis Ioannidis and Christos Tjortjis
Algorithms 2023, 16(10), 482; https://doi.org/10.3390/a16100482 - 17 Oct 2023
Viewed by 1336
Abstract
The Smart Readiness Indicator (SRI) is a newly developed framework that measures a building’s technological readiness to improve its energy efficiency. The integration of data obtained from this framework with data derived from Building Information Modeling (BIM) has the potential to yield compelling [...] Read more.
The Smart Readiness Indicator (SRI) is a newly developed framework that measures a building’s technological readiness to improve its energy efficiency. The integration of data obtained from this framework with data derived from Building Information Modeling (BIM) has the potential to yield compelling results. This research proposes an algorithm for a Recommendation System (RS) that uses SRI and BIM data to advise on building energy-efficiency improvements. Following a modular programming approach, the proposed system is split into two algorithmic approaches linked with two distinct use cases. In the first use case, BIM data are utilized to provide thermal envelope enhancement recommendations. A hybrid Machine Learning (ML) (Random Forest–Decision Tree) algorithm is trained using an Industry Foundation Class (IFC) BIM model of CERTH’S nZEB Smart Home in Greece and Passive House database data. In the second use case, SRI data are utilized to develop an RS for Heating, Ventilation, and Air Conditioning (HVAC) system improvement, in which a process utilizes a filtering function and KNN algorithm to suggest automation levels for building service improvements. Considering the results from both use cases, this paper provides a solid framework that exploits more possibilities for coupling SRI with BIM data. It presents a novel algorithm that exploits these data to facilitate the development of an RS system for increasing building energy efficiency. Full article
(This article belongs to the Special Issue Self-Learning and Self-Adapting Algorithms in Machine Learning)
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38 pages, 28537 KiB  
Article
Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach
by Claudio Szwarcfiter, Yale T. Herer and Avraham Shtub
Algorithms 2023, 16(8), 395; https://doi.org/10.3390/a16080395 - 21 Aug 2023
Viewed by 1539
Abstract
Industrial projects are plagued by uncertainties, often resulting in both time and cost overruns. This research introduces an innovative approach, employing Reinforcement Learning (RL), to address three distinct project management challenges within a setting of uncertain activity durations. The primary objective is to [...] Read more.
Industrial projects are plagued by uncertainties, often resulting in both time and cost overruns. This research introduces an innovative approach, employing Reinforcement Learning (RL), to address three distinct project management challenges within a setting of uncertain activity durations. The primary objective is to identify stable baseline schedules. The first challenge encompasses the multimode lean project management problem, wherein the goal is to maximize a project’s value function while adhering to both due date and budget chance constraints. The second challenge involves the chance-constrained critical chain buffer management problem in a multimode context. Here, the aim is to minimize the project delivery date while considering resource constraints and duration-chance constraints. The third challenge revolves around striking a balance between the project value and its net present value (NPV) within a resource-constrained multimode environment. To tackle these three challenges, we devised mathematical programming models, some of which were solved optimally. Additionally, we developed competitive RL-based algorithms and verified their performance against established benchmarks. Our RL algorithms consistently generated schedules that compared favorably with the benchmarks, leading to higher project values and NPVs and shorter schedules while staying within the stakeholders’ risk thresholds. The potential beneficiaries of this research are project managers and decision-makers who can use this approach to generate an efficient frontier of optimal project plans. Full article
(This article belongs to the Special Issue Self-Learning and Self-Adapting Algorithms in Machine Learning)
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14 pages, 3163 KiB  
Article
Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers
by Claudia Di Napoli, Giovanni Paragliola, Patrizia Ribino and Luca Serino
Algorithms 2023, 16(8), 362; https://doi.org/10.3390/a16080362 - 28 Jul 2023
Viewed by 1325
Abstract
The increasing popularity of cruise tourism has led to the need for effective planning and management strategies to enhance the city tour experience for cruise passengers. This paper presents a deep reinforcement learning (DRL)-based planner specifically designed to optimize city tours for cruise [...] Read more.
The increasing popularity of cruise tourism has led to the need for effective planning and management strategies to enhance the city tour experience for cruise passengers. This paper presents a deep reinforcement learning (DRL)-based planner specifically designed to optimize city tours for cruise passengers. By leveraging the power of DRL, the proposed planner aims to maximize the number of visited attractions while considering constraints such as time availability, attraction capacities, and travel distances. The planner offers an intelligent and personalized approach to city tour planning, enhancing the overall satisfaction of cruise passengers and minimizing the negative impacts on the city’s infrastructure. An experimental evaluation was conducted considering Naples’s fourteen most attractive points of interest. Results show that, with 30 state variables and more than 19∗1012 possible states to be explored, the DRL-based planner converges to an optimal solution after only 20,000 learning steps. Full article
(This article belongs to the Special Issue Self-Learning and Self-Adapting Algorithms in Machine Learning)
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24 pages, 1704 KiB  
Article
Model and Training Method of the Resilient Image Classifier Considering Faults, Concept Drift, and Adversarial Attacks
by Viacheslav Moskalenko, Vyacheslav Kharchenko, Alona Moskalenko and Sergey Petrov
Algorithms 2022, 15(10), 384; https://doi.org/10.3390/a15100384 - 19 Oct 2022
Cited by 3 | Viewed by 2285
Abstract
Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on model performance. This paper proposes a model and training method [...] Read more.
Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on model performance. This paper proposes a model and training method for a resilient image classifier capable of efficiently functioning despite various faults, adversarial attacks, and concept drifts. The proposed model has a multi-section structure with a hierarchy of optimized class prototypes and hyperspherical class boundaries, which provides adaptive computation, perturbation absorption, and graceful degradation. The proposed training method entails the application of a complex loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of new labeled and unlabeled data. The training method implements principles of self-knowledge distillation, the compactness maximization of class distribution and the interclass gap, the compression of feature representations, and consistency regularization. Consistency regularization makes it possible to utilize both labeled and unlabeled data to obtain a robust model and implement continuous adaptation. Experiments are performed on the publicly available CIFAR-10 and CIFAR-100 datasets using model backbones based on modules ResBlocks from the ResNet50 architecture and Swin transformer blocks. It is experimentally proven that the proposed prototype-based classifier head is characterized by a higher level of robustness and adaptability in comparison with the dense layer-based classifier head. It is also shown that multi-section structure and self-knowledge distillation feature conserve resources when processing simple samples under normal conditions and increase computational costs to improve the reliability of decisions when exposed to perturbations. Full article
(This article belongs to the Special Issue Self-Learning and Self-Adapting Algorithms in Machine Learning)
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