Artificial Intelligence as a Catalyst for Management System Adaptability, Agility and Resilience: Mapping the Research Agenda by Ion Popa, Simona Cătălina Ștefan, Andrei Josan, Corina-Elena Mircioiu, Nicoleta Căruceru

“…Likewise, its thematic and strategic evolution is characterized as a surprising one, managing to incorporate and relate concepts with a strong technical and IT character such as feature extraction, machine learning, reinforcement learning with concepts of a managerial nature as supporting customer-tailored interaction, employee skills development, company productivity, and innovation.…”
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Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning by Ayoub Alsarhan, Anjali Agarwal

“…These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. …”
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Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System by Qinmu Xie, Shoufeng Ma, Ning Jia, Yang Gao

“…By using multiagent modeling and the Bush-Mosteller reinforcement learning model, we simulated the day-to-day evolution of commuters’ departure time choice on a many-to-one mass transit system during the morning peak period. …”
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Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System by Takashi Kuremoto, Takahito Komoto, Kunikazu Kobayashi, Masanao Obayashi

“…The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. …”
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Modeling and Optimization of Multiaction Dynamic Dispatching Problem for Shared Autonomous Electric Vehicles by Ning Wang, Jiahui Guo

“…Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.…”
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Bridging theory and practice in peer-to-peer energy trading: market mechanisms and technological innovations by Pravesh Raghoo, Kalim Shah

“…As such, three market designs are discussed: centralized, decentralized, and distributed, and four pricing mechanisms, which are optimization, game theory, auction-based, and reinforcement learning. Enabling technologies discussed are Energy Internet, Internet of Things, Artificial intelligence, Blockchain, Communication networks, and battery flexibility. …”
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Computerized Adaptive Testing Framework Based on Excitation Block and Gumbel-Softmax by Chengsong Liu, Yan Wei

“…Additionally, it views CAT as reinforcement learning, introducing Gumbel-Softmax to provide students with diverse, non-repetitive, and valuable test questions. …”
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Minimizing Delay and Power Consumption at the Edge by Erol Gelenbe

“…Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. …”
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An energy management strategy for integrated electricity-thermal energy systems using the DQN-CE algorithm by ZHU Jiejie, PI Zhiyong, CHEN Daicai, TAN Hong

“…To address the uncertainty and intermittency of renewable energy output in integrated electricity-thermal energy systems， a reinforcement learning method for energy management is proposed， aiming to minimize the operating costs of the system. …”
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Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning by Zahra Saeidi Mobarakeh, Hossein Amoozadkhalili

“…This fuzzy logic is combined with machine learning algorithms such as neural networks and reinforcement learning to create an intelligent and flexible model that effectively adapts to sudden changes in dynamic environments. …”
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Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction by Simeon Karpuzov, George Petkov, Stiliyan Kalitzin

“…This is achieved by automated reinforcement learning and simultaneously applying the interdependences between the cameras. …”
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Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning by J. Arun Pandian, Ramkumar Thirunavukarasu, L. Thanga Mariappan

“…The proposed technique optimizes the segmentation accuracy and treats the attained accuracy as a reward signal in the context of reinforcement learning by interacting with the environment through CNN model selection. …”
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Mixture of Experts Framework Based on Soft Actor-Critic Algorithm for Highway Decision-Making of Connected and Automated Vehicles by Fuxing Yao, Chao Sun, Bing Lu, Bo Wang, Haiyang Yu

“…Abstract Decision-making of connected and automated vehicles (CAV) includes a sequence of driving maneuvers that improve safety and efficiency, characterized by complex scenarios, strong uncertainty, and high real-time requirements. Deep reinforcement learning (DRL) exhibits excellent capability of real-time decision-making and adaptability to complex scenarios, and generalization abilities. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…These algorithms analyze real-time data from sensors and IoT devices to predict energy demand, enabling dynamic load balancing and reducing waste. Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. …”
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Delta opioid receptors affect acoustic features of song during vocal learning in zebra finches by Utkarsha A. Singh, Soumya Iyengar

“…We wanted to study if they were also involved in naturally-occurring reinforcement learning behaviors such as vocal learning, using the zebra finch model system. …”
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Collaborative forecasting management model for multi‐energy microgrid considering load response characterization by Huiyu Bao, Yi Sun, Jie Peng, Xiaorui Qian, Peng Wu

“…The model combines a multi‐energy load prediction model with a management model based on deep reinforcement learning. It proposes multiple iterations of data, fits the dynamic environment of MEMG by continuously improving the long short‐term memory (LSTM) neural network based on knowledge distillation (KD) architecture, and then optimizes the MEMG state space by considering the knowledge of load response characteristics, Furthermore, it combines multi‐agent deep deterministic policy gradient (MADDPG) with horizontal federated (hF) learning to co‐train multi‐MEMG, addressing the issues of training efficiency during co‐training. …”
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Composition of Web Services Using Markov Decision Processes and Dynamic Programming by Víctor Uc-Cetina, Francisco Moo-Mena, Rafael Hernandez-Ucan

“…Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity.…”
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SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication by Yusuf Kursat Tuncel, Kasım Öztoprak

“…SAFE-CAST integrates several innovative components: (1) a federated learning approach using Lloyd’s K-means algorithm for secure clustering, (2) a quality diversity optimization algorithm (QDOA) for secure channel selection, (3) a dynamic trust management system utilizing blockchain technology, and (4) an adaptive multi-agent reinforcement learning for context-aware transmission scheme (AMARLCAT) to minimize latency and improve scalability. …”
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Sliding Mode Control for Variable-Speed Trajectory Tracking of Underactuated Vessels with TD3 Algorithm Optimization by Shiya Zhu, Gang Zhang, Qin Wang, Zhengyu Li

“…An adaptive sliding mode controller (SMC) design with a reinforcement-learning parameter optimization method is proposed for variable-speed trajectory tracking control of underactuated vessels under scenarios involving model uncertainties and external environmental disturbances. …”
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A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination by Xiaoke Zhou, Fei Zhu, Quan Liu, Yuchen Fu, Wei Huang

“…Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. …”
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