The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s na...
The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approa...
In the context of distributed defense, multi-sensor networks are required to be able to carry out reasonable planning and scheduling to achieve the purpose of continuous, accurate and rapid target detection. In this paper, a multi-sensor cooperative...
Corrosion is one of the major causes of failure in pipelines for transporting oil and gas products. To mitigate the impact of this problem, organizations perform different maintenance operations, including detecting corrosion, determining corrosion g...
Inducing learning strategies is a crucial component of intelligent tutoring systems. Previous research has predominantly focused on the induction of offline learning strategies. Although the existing offline learning strategy induction methods can al...
This paper proposes a continuous state partially observable Markov decision process (POMDP) model for the corrosion maintenance of oil and gas pipelines. The maintenance operations include complex and extensive activities to detect the corrosion type...
The Ka-band and higher Q/V band channels can provide an appealing capacity for the future deep-space communications and Space Information Networks (SIN), which are viewed as a primary solution to satisfy the increasing demands for high data rate serv...
Autonomous shuttles have been used as end-mile solutions for smart mobility in smart cities. The urban driving conditions of smart cities with many other actors sharing the road and the presence of intersections have posed challenges to the use of au...
As network systems become larger and more complex, there is an increasing focus on how to verify the security of systems that are at risk of being attacked. Automated penetration testing is one of the effective ways to achieve this. Uncertainty cause...
Deep reinforcement learning (DRL)-based navigation in an environment with dynamic obstacles is a challenging task due to the partially observable nature of the problem. While DRL algorithms are built around the Markov property (assumption that all th...
During the last decades, collaborative robots capable of operating out of their cages are widely used in industry to assist humans in mundane and harsh manufacturing tasks. Although such robots are inherently safe by design, they are commonly accompa...
Screening for cervical cancer is a critical policy that requires clinical and managerial vigilance because of its serious health consequences. Recently the practice of conducting simultaneous tests of cytology and Human Papillomavirus (HPV)-DNA testi...
Fault recovery in distribution networks is a complex, high-dimensional decision-making task characterized by partial observability, dynamic topology, and strong interdependencies among components. To address these challenges, this paper proposes a gr...
Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been...
Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable mod...
Particle Swarm Optimization (PSO) is a widespread evolutionary technique that has successfully solved diverse optimization problems across various application fields. However, when dealing with more complex optimization problems, PSO can suffer from...
The development of artificial intelligence (AI) game agents that use deep reinforcement learning (DRL) algorithms to process visual information for decision-making has emerged as a key research focus in both academia and industry. However, previous g...
During the process of disease diagnosis, overdiagnosis can lead to potential health loss and unnecessary anxiety for patients as well as increased medical costs, while underdiagnosis can result in patients not being treated on time. To deal with thes...
Geospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions. We explore the Earth’s geomagnetic field, a globally distributed and naturally occurr...
Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for...
Grid-based maritime search and rescue (SAR) planning requires sequential decisions from incomplete spatial observations. Increasing map size also expands the search space and can intensify reward sparsity. This paper proposes CCAL-DQN, an LSTM deep Q...
Unmanned Aerial Vehicles (UAV) can navigate with low risk in obstacle-free environments using ground control stations that plan a series of GPS waypoints as a path to follow. This GPS waypoint navigation does however become dangerous in environments...
Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). Ho...
The increasing penetration of inverter-interfaced photovoltaic (PV) generation in active distribution networks (ADNs) intensifies fast voltage violations and makes real-time Volt-VAR control (VVC) challenging, especially when each inverter has only p...
To address the limited endurance of Unmanned Surface Vehicles (USVs) in time-varying sea conditions, this study investigates global energy-efficient path planning that balances obstacle avoidance and energy efficiency. First, based on ship seakeeping...
To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrat...
To tackle frequent voltage violations in distribution networks with high photovoltaic penetration ratios, voltage regulation methods that dispatch the active and reactive powers of PV panels are widely studied. Because of the costly global communicat...
Autonomous Unmanned Aerial Vehicles (UAVs) have possible applications in wildlife monitoring, disaster monitoring, and emergency Search and Rescue (SAR). Autonomous capabilities such as waypoint flight modes and obstacle avoidance, as well as their a...
Background/Objectives: AI-assisted documentation and coding tools add diagnoses to health records, and self-updating AI agents may soon learn from the codes they generate. The objective was to develop a framework for asking whether such systems could...
This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments ca...
Current electronic warfare jammers and radar countermeasures are characterized by dynamism and uncertainty. This paper focuses on a decision-making framework of radar anti-jamming countermeasures. The characteristics and implementation process of rad...
Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncert...
This research investigates the joint optimization of maintenance and spare unit management for series systems composed of multiple heterogeneous units. With advancements in communication and sensing technologies, condition-based maintenance has gaine...
The operation of a variety of natural or man-made systems subject to uncertainty is maintained within a range of safe behavior through run-time sensing of the system state and control actions selected according to some strategy. When the system is ob...
For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales u...
An intelligent land vehicle utilizes onboard sensors to acquire observed states at a disorderly intersection. However, partial observation of the environment occurs due to sensor noise. This causes decision failure easily. A collision relationship-ba...
Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring determines the time between battery charges of the wearable sensors, which is a key performa...
Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidd...
Aiming to improve the efficiency of the online process in path planning, a novel searching method is proposed based on environmental information analysis. Firstly, a search and rescue (SAR) environmental model and an unmanned ground vehicle (UGV) mot...
Autonomy in Unmanned Aerial Vehicle (UAV) navigation has enabled applications in diverse fields such as mining, precision agriculture, and planetary exploration. However, challenging applications in complex environments complicate the interaction bet...
Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimizati...
Moving-target detection under strict sensing constraints is a recurring subproblem in surveillance, search-and-rescue, and autonomous robotics. We study a canonical one-dimensional finite grid in which a sensor probes one location per time step with...
Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder...
Low, small, and slow (LSS) unmanned aerial vehicles (UAVs) pose great challenges for conventional guidance methods. However, existing deep reinforcement learning (DRL)-based interception guidance law has mostly focused on simplified two-dimensional p...
With the development of value decomposition methods, multi-agent reinforcement learning (MARL) has made significant progress in balancing autonomous decision making with collective cooperation. However, the collaborative dynamics among agents are con...
We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decision...
Autonomous network management requires balancing rapid incident resolution with strict operational safety. This paper introduces a production-inspired, end-to-end multi-agent orchestration architecture for network fault diagnosis and resolution. Buil...
We develop Bayesian Mechanics Portfolio Theory (BMPT), which represents an investor as an agent that infers hidden market states, encodes preferences, evaluates policies by expected free energy, and acts. We first prove a boundary theorem showing tha...
Coordinating multiple unmanned aerial vehicles (UAVs) for the purposes of target finding or surveying points of interest in large, complex, and partially observable environments remains an area of exploration. This work proposes a modeling approach a...
In order to make wind energy more competitive, the big expenses for operation and maintenance must be reduced. Consistent decisions that minimize the expected costs can be made based on risk-based methods. Such methods have been implemented for maint...
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